CN109270407A - Extra high voltage direct current transmission line failure cause discrimination method based on Multi-source Information Fusion - Google Patents
Extra high voltage direct current transmission line failure cause discrimination method based on Multi-source Information Fusion Download PDFInfo
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Abstract
The present invention relates to a kind of extra high voltage direct current transmission line failure cause discrimination method based on Multi-source Information Fusion comprising: obtain the electrical quantity fault data and non-electric quantity information of extra high voltage direct current transmission line；Extra high voltage direct current transmission line electric characteristic amount is extracted, electric characteristic input vector is constructed；Extra high voltage direct current transmission line non-electrical characteristic quantity is extracted, non-electrical feature input vector is constructed；Global neurological network identification model is constructed respectively to lightning stroke, mountain fire, filth, windage yaw, bird pest, the number of plies of the hidden layer of neural network model is chosen using the method for minimal error；Specific failure cause identification is carried out using the method for self study.Universal electric amount information and non-electric quantity information of the present invention, find characteristic rule, multi-source information merge using the amalgamation of neural network itself, utilize the thought of big data method, utilize multi-source fault message, in conjunction with neural network algorithm, the identification of reason is carried out, has fully ensured that the accuracy of reason identification.
Description
Technical field
The present invention relates to ultra-high-tension power transmission line O&M field, specifically a kind of extra-high voltage based on Multi-source Information Fusion
Direct current transmission line fault reason discrimination method.
Background technique
Extra high voltage direct current transmission line length is located at the locations such as mountain area hills up to thousands of kilometers mostly, is easy by each
The influences such as kind natural calamity, external force destruction are broken down, and timely the Research on Identification of failure cause can instruct line walking, add
Fast maintenance and line powering restore.
Transmission line malfunction mainly includes the failure as caused by the reasons such as lightning stroke, mountain fire, filth, bird pest and windage yaw.To event
The identification for hindering cause needs premised on the understanding to various failure principles and process, on this basis to the spy of specific fault
Sign carries out mining analysis, so as to form the foundation of reason identification.
Wherein, fault signature includes the fault signature of electrical quantity and the fault signature of non-electric quantity, the electricity under different reasons
Tolerance feature is there is similitude, and there is also distinctive factor, relying solely on a kind of factor can not accurately know non-electric quantity
Not.There is presently no the methods for the identification of extra high voltage direct current transmission line failure cause of complete set, send out in transmission line of electricity
After raw failure, the auxiliary in decision can not be provided to operating maintenance personnel.It is a kind of based on electrical quantity and non-it is therefore desirable to provide
The extra high voltage direct current transmission line failure cause discrimination method of the Multi-source Information Fusions such as electrical quantity.
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 lines based on Multi-source Information Fusion
Failure cause discrimination method, this method combination electric quantity information and non-electric quantity information find characteristic rule, are subsequent nerve
The foundation of network class model provides data source, meanwhile, the present invention construct the neural network of failure cause identification structure and
Above multi-source information merge by whole cause identification framework using the amalgamation of neural network itself, utilizes big number
According to the thought of method, the identification of reason is carried out, has fully ensured that reason in conjunction with neural network algorithm using multi-source fault message
The accuracy of identification.
In order to solve the above technical problems, the extra high voltage direct current transmission line failure of the invention based on Multi-source Information Fusion is former
Because of discrimination method, include the following steps:
The electrical quantity fault data and non-electric quantity information of step 1) acquisition extra high voltage direct current transmission line；
Step 2) extracts extra high voltage direct current transmission line electric characteristic amount, constructs electric characteristic input vector；
Step 3) extracts extra high voltage direct current transmission line non-electrical characteristic quantity, constructs non-electrical feature input vector；
Step 4) constructs global neurological network identification model, nerve net to lightning stroke, mountain fire, filth, windage yaw, bird pest respectively
The number of plies of the hidden layer of network model is chosen using the method for minimal error；
Step 5) carries out specific failure cause identification using the method for self study.
In step 1), the electrical quantity fault data of extra high voltage direct current transmission line is obtained from protection information system, non-
Electric quantity information is obtained from meteorological system, lightning location system, GIS-Geographic Information System, forest fire monitoring system；When the event of generation
Barrier seldom, when the lazy weight of sample, then obtains data by PSACD simulated fault.
In step 2), if it is t that the moment, which occurs, for failure1, extract the data after extra high voltage direct current transmission line breaks down
As electric characteristic amount, electric characteristic amount includes fault current maximum amplitude, the feature of voltage and current amplitude frequency diagram, transition resistance
Mean value, transition resistance standard deviation；
Fault current maximum amplitude is | I |；Wherein electric current I is the value of fault current, is positive or is negative；
The feature of voltage and current amplitude frequency diagram needs to carry out discrete fourier to data to decompose to obtain, according to formula
To Current Voltage Fourier transformation, the waveform of frequency domain is obtained, and then extracts and obtains the feature of voltage and current amplitude frequency diagram
Amount；
According to formula
Obtain transition resistance mean value；
According to formula
Transition resistance standard deviation E；Just electric characteristic input vector has been obtained.
In step 3), by non-electrical characteristic quantity be divided into weather, the period, season, landform, lightening activity frequency, temperature,
9 humidity, wind-force and historical failure information characteristic quantities, are indicated by orderly binary number, establish the input of non-electrical feature
Vector.
In step 4), using three layers of BP neural network structure, input layer number is 33, and hidden layer is missed using minimum
The method of difference is chosen, and output layer number of nodes is 5；The activation primitive of hidden layer and output layer is all made of log-sigmoid type letter
Number, can be by output control within the scope of 0-1；Training method uses trainlm (Levenberg-Marquardt algorithm),
Training objective error is set as 1e-4, and frequency of training is set as 1000 times.
BP neural network is according to the multilayer feedforward neural network of error backpropagation algorithm training, if input layer includes N
A neuron, output layer include M neuron, and input vector X, output vector Y, hidden layer includes L neuron section
Point, wijIndicate input layer to the connection weight between hidden layer, wjkIndicate hidden layer to the connection weight between output layer, θj
Indicate the threshold value of hidden layer neuron, θkIndicate the threshold value of output layer neuron,Indicate the activation primitive of hidden layer, φ
The activation primitive of () expression output layer.
The logarithmic function of S type are as follows:
The output signal o of j-th of neuron node of hidden layerjAre as follows:
The output signal y of k-th of neuron node of output layerkAre as follows:
Equipped with P training sample, for each training sample, the quadratic form criterion function of error are as follows:
Global error function of the network to P training sample are as follows:
WhereinWithRespectively indicate input training sample be p when, the expectation of k-th of neuron node of output layer
Output and reality output.
It is to have difference between practical identification result and ideal output, with overall Identification Errors EbTo characterize this species diversity:
Ebi=Rsi-Rqi (11)
Wherein i=1~5 respectively indicate lightning stroke, mountain fire, filth, windage yaw, bird pest；Rsi、RqiRespectively indicate such reason
Under reality output and desired output, EbiFor the reality output under such reason and the difference between desired output；
The range of a hidden layer is determined according to the empirical equation of hidden layer:
In formula, m and n points of ratios are the number of nodes for outputting and inputting layer, integer of a between 0-10；
The determination step of best hidden layer are as follows:
Step 4.1) calculates the value range of l, and the range of l is (6,16)；
Step 4.2) the implicit number of plies possible for each in l calculates Ebi、Eb；
Step 4.3) compares overall Identification Errors E corresponding to each possible implicit number of plies in lb, take the smallest
EbThe corresponding implicit number of plies is best hidden layer.It should be noted that for different training samples, the number of best hidden layer
Amount is not necessarily identical.
In step 5), when failure occurs, failure cause is differentiated using the network model of creation, troubleshooting knot
Shu Hou, using the failure of generation as standard failure type typing historical failure data library, and by actual failure cause and model
Differentiate that result compares, whether comparing result is as modifying the foundation in neural metwork training library；As overall Identification Errors Eb>
When 0.5, the sample set in actual failure cause model training library is modified, the actual characteristic data that the secondary failure is added carry out weight
New training, obtains revised neural network prediction model, so as to autonomous learning, adapts to the variation of environment, more accurately
Identification of defective reason；If differentiating result and actual result deviation within the allowable range, do not need to modify in trained library
Sample, but record the fault signature and failure cause.
The step of neural metwork training are as follows:
The formation of step 5.1) training sample, according to the electric characteristic amount and non-electrical feature in step 2) and step 3)
The extraction of amount, constructs comprehensive input feature value, and input vector is input 1~input 33, the failure cause according to known to sample
Type structure output vector, and then input vector and output vector are mapped composing training sample；
Step 5.2) initially takes l=6, and input layer, hidden layer, output layer are successively passed through in the forward-propagating of signal；
Step 5.3) calculate output error, if target error meets setting value 1e-3, complete training output as a result,
Otherwise the 4th step is carried out；
The backpropagation of step 5.4) signal successively corrects Feedback error according to additional guide vanes trainlm
Each layer of weight and threshold value；
Step 5.5) repeats step 5.2), step 5.3), step 5.4) according to new weight and threshold value, until error is full
Sufficient setting value；Judge whether l takes, step 5.6) is carried out if taking, otherwise l+1, return step 5.2)；
Step 5.6) compares corresponding to each possible implicit number of plies in l according to the determination method of best hidden layer
Overall Identification Errors Eb, take the smallest EbThe corresponding implicit number of plies is best hidden layer；
Step 5.7) carries out real case test, and after best hidden layer determines, the structure of neural network is just determined, will be real
The fault condition on border is input in neural network, the result recognized；
Step 5.8) uses self-learning algorithm, and every time after identification, actual failure cause and prediction model are differentiated result
It compares, whether comparing result is as modifying the foundation in neural metwork training library；If differentiating that result is deviated,
The sample set for modifying actual failure cause model training library, the actual characteristic data that the secondary failure is added carry out re -training,
Obtain revised neural network prediction model.
The beneficial effects of the present invention are: the generation of failure is related with the running environment of transmission line of electricity, and variety classes failure
Feature show difference on Wave data, therefore to line fault occurs for this method the moment on the basis of principle analysis
The non-electric quantities factors such as weather condition, the landform in period, season and scene, historical data and failure pole tension,
The internal factor of the electrical measure feature such as electric current, transition resistance, frequency domain distribution is excavated, its characteristic rule is found, thus after being
The foundation of continuous neural network classification model provides data source.Meanwhile the present invention constructs the neural network of failure cause identification
Structure and whole cause identification framework, this method using neural network itself amalgamation by above multi-source information into
Row fusion, using the thought of big data method, carries out distinguishing for reason in conjunction with neural network algorithm using multi-source fault message
Know, has fully ensured that the accuracy of reason identification.
Detailed description of the invention
Invention is further described in detail with reference to the accompanying drawings and detailed description:
Fig. 1 is the false voltage amplitude frequency diagram after discrete Fourier transform, time domain is transformed into frequency, before containing
The electric fault characteristic quantity stated: voltage wave cut off, voltage wave direct current amplitude；
Fig. 2 is the fault current amplitude frequency diagram after discrete Fourier transform, time domain is transformed into frequency, before containing
The electric fault characteristic quantity stated: current wave cut off, current wave direct current amplitude；
Fig. 3 is global neurological network identification illustraton of model, totally 33 input vectors, and wherein 1-7 is electrical quantity characteristic value, 8-
33 be non-electric quantity characteristic value, and output result is one of lightning stroke, mountain fire, filth, windage yaw, bird pest；
Fig. 4 is neural network model figure, structure 33-L-5；
Fig. 5 is Neural Network Self-learning flow chart.
Specific embodiment
Referring to attached drawing, the extra high voltage direct current transmission line failure cause identification side of the invention based on Multi-source Information Fusion
Method includes the following steps:
The electrical quantity fault data and non-electric quantity information of step 1) acquisition extra high voltage direct current transmission line；
Step 2) extracts extra high voltage direct current transmission line electric characteristic amount, constructs electric characteristic input vector；
Step 3) extracts extra high voltage direct current transmission line non-electrical characteristic quantity, constructs non-electrical feature input vector；
Step 4) constructs global neurological network identification model, nerve net to lightning stroke, mountain fire, filth, windage yaw, bird pest respectively
The number of plies of the hidden layer of network model is chosen using the method for minimal error；
Step 5) carries out specific failure cause identification using the method for self study.
In step 1), the fault data of extra high voltage direct current transmission line electrical quantity is obtained from protection information system, non-electrical
The information of tolerance is obtained from meteorological system, lightning location system, GIS-Geographic Information System, forest fire monitoring system etc..When generation
Failure is seldom, when the lazy weight of sample, then obtains data by PSACD simulated fault.
In step 2), occurs for failure for t the moment1, this method extract extra high voltage direct current transmission line break down after number
According to the mould of the maximum amplitude including fault current, the feature of voltage and current amplitude frequency diagram, transition resistance mean value, transition resistance standard
Difference is used as electrical quantity fault eigenvalue.The feature of voltage and current amplitude frequency diagram needs to carry out discrete fourier to data to decompose to obtain.
Fault current maximum amplitude is | I |；Wherein electric current I is the value of fault current, and can be positive can be negative.
DC current sampled value i (n), n=0,1 after taking failure, 2 ... .N-1.Since current sampling data is discrete
Value, can be broken down into n times harmonic wave with discrete Fourier transform (DFT).
Discrete Fourier transform principle:
K=0 in formula, 1,2 ... N-1 are overtone order, and N is the total number of sample points of a cycle.
Its fundametal compoment is calculated as follows:
Its DC component is calculated as follows:
Similarly, can also discrete Fourier transform be carried out to voltage.
For this method, voltage, electric current are a DC quantity before failure occurs, and failure has a lot humorous after occurring
Wave component is distributed within a very wide frequency range, by discrete Fourier transform (DFT) by time domain after failure
Waveform is transformed into frequency domain and is analyzed, to obtain the characteristic quantity under certain frequency domains.
This method illustrates the method by extracting corresponding failure characteristic value to voltage, electric current amplitude frequency diagram for being struck by lightning.
Fig. 1 and Fig. 2 is respectively the false voltage and fault current progress discrete Fourier transform after being struck by lightning to route
Voltage, electric current amplitude frequency diagram afterwards.
As seen in Figure 1, the content of DC component is maximum, and harmonic wave focuses mostly within 1kHZ, humorous higher than 1kHZ
Wave is seldom.This method is using 1KHZ as a characteristic value, and referred to as " voltage wave cut off ", while the amplitude of flip-flop is also made
For a characteristic value.
As seen in Figure 2, the content of DC component is maximum, and harmonic wave focuses mostly within 10HZ, humorous higher than 10HZ
Wave is seldom.This method is using 10HZ as a characteristic value, and referred to as " current wave cut off ", while the amplitude of flip-flop is also made
For a characteristic value.
Using the average R of transition resistance instantaneous value after failure, reflect the resistance value size characteristic of transition resistance, wherein R
Are as follows:
Transition resistance standard deviation E are as follows:
Wherein i=1,2,3....N, N are total number of sample points.
Therefore electric characteristic vector X1-X7 has just been obtained.
In step 3), the present invention by non-electric quantity be divided into weather, the period, season, landform, lightening activity frequency, temperature,
9 humidity, wind-force and historical failure information characteristic quantities, are indicated by orderly binary number, establish the feature of non-electric quantity
Input vector, input vector are input 8- input 33.
Construction method is as follows:
1, the characteristic quantity input model of weather condition
Weather condition is set as four kinds of situations, is respectively as follows: fine day, cloudy day, misty rain, thunderstorm.Lightning stroke mostly occurs in thunderstorm
Weather, this method indicates this four characteristic quantities with 4 bits, for example, using vector
[1 00 0] indicate fine day.Characteristic quantity input model are as follows:
| Input 8 | Input 9 | Input 10 | Input 11 | |
| Fine day | Cloudy day | Misty rain | Thunderstorm | Corresponding types |
| 1 | 0 | 0 | 0 | Fine day |
| 0 | 1 | 0 | 0 | Cloudy day |
| 0 | 0 | 1 | 0 | Misty rain |
| 0 | 0 | 0 | 1 | Thunderstorm |
2, period characteristic quantity input model
This method indicates this four characteristic quantities with 4 bits, and early morning (when -8 when 4), daytime are (- 17 when 9
When), at dusk (when -21 when 18), midnight (when -3 when 22).For example, early morning is indicated with vector [1 00 0], characteristic quantity input
Model are as follows:
| Input 12 | Input 13 | Input 14 | Input 15 | |
| Early morning | Daytime | At dusk | Midnight | Corresponding types |
| 1 | 0 | 0 | 0 | Early morning |
| 0 | 1 | 0 | 0 | Daytime |
| 0 | 0 | 1 | 0 | At dusk |
| 0 | 0 | 0 | 1 | Midnight |
3, seasonal characteristic quantity input model
This method indicates spring, summer, autumn, this four characteristic quantities of winter with 4 bits, for example, using vector
[1 00 0] indicate spring.Characteristic quantity input model are as follows:
| Input 16 | Input 17 | Input 18 | Input 19 | |
| Spring | Summer | Autumn | Winter | Corresponding types |
| 1 | 0 | 0 | 0 | Spring |
| 0 | 1 | 0 | 0 | Summer |
| 0 | 0 | 1 | 0 | Autumn |
| 0 | 0 | 0 | 1 | Winter |
4, features of terrain
Features of terrain is divided into Plain, hills, mountain area three types by this method, is indicated with 3 bits
These three characteristic quantities, for example, indicating Plain with vector [1 0 0].Characteristic quantity input model are as follows:
| Input 20 | Input 21 | Input 22 | |
| Plain | Hills | Mountain area | Corresponding types |
| 1 | 0 | 0 | Plain |
| 0 | 1 | 0 | Hills |
| 0 | 0 | 1 | Mountain area |
5, lightening activity frequency (thunderstorm day)
It is considered as area with less lightning activities that thunderstorm day Td, which is equal to 15 areas below, and the area more than 40 is area with more lightning activities, more than 90
Area is special strong minefield.This method indicates these three characteristic quantities with 3 bits, for example, with vector [1 0
0] area with less lightning activities is indicated.Characteristic quantity input model are as follows:
| Input 23 | Input 24 | Input 25 | |
| Area with less lightning activities | Area with more lightning activities | Strong minefield | Corresponding types |
| 1 | 0 | 0 | Area with less lightning activities |
| 0 | 1 | 0 | Area with more lightning activities |
| 0 | 0 | 1 | Strong minefield |
6, temperature, humidity, wind-force
| Input 26 | Input 27 | Input 28 |
| Temperature | Humidity | Wind-force |
| X1 | X2 | X3 |
The unit of temperature is degree Celsius that humidity is percentage, and wind-force is 0-17 grades, and required parameter is obtained from meteorological system
?.
7, historical failure information
Historical failure information is divided into 5 classes according to fault type by this method: lightning stroke, mountain fire, filth, windage yaw, bird pest.
Need to record be break down place ownership certain power supply company restriction occurred in history such failure time
Number, is denoted as N1~N5, represents number.For example, somewhere occurred lightning fault 2 times in history, then N1=2 is remembered.
| Input 29 | Input 30 | Input 31 | Input 32 | Input 33 |
| Lightning stroke | Mountain fire | It is filthy | Windage yaw | Bird pest |
| N1 | N2 | N3 | N4 | N5 |
Step (4), the building of the global neurologicals network identification model such as lightning stroke, mountain fire, filth, windage yaw, bird pest, synthesis are distinguished
Know model such as Fig. 3.This method uses three layers of BP neural network structure, and input layer number is 33, and hidden layer uses minimal error
Method choose.Output layer number of nodes is 5.Neural network structure such as Fig. 4.The activation primitive of hidden layer and output layer is all made of
Log-sigmoid type function (logarithmic function of S type), can be by output control within the scope of 0-1.Training method uses
Trainlm (Levenberg-Marquardt algorithm), training objective error are set as 1e-3, and maximum frequency of training is set as
1000 times.
BP neural network is according to the multilayer feedforward neural network of error backpropagation algorithm training, such as Fig. 4, input layer
Comprising N number of neuron, output layer includes M neuron, and input vector X, output vector Y, hidden layer includes L nerve
First node, wijIndicate input layer to the connection weight between hidden layer, wjkIndicate hidden layer to the connection weight between output layer
Value, θjIndicate the threshold value of hidden layer neuron, θkIndicate the threshold value of output layer neuron,Indicate the activation letter of hidden layer
Number, φ () indicate the activation primitive of output layer.
The logarithmic function of S type are as follows:
The output signal o of j-th of neuron node of hidden layerjAre as follows:
The output signal y of k-th of neuron node of output layerkAre as follows:
Equipped with P training sample, for each training sample, the quadratic form criterion function of error are as follows:
Global error function of the network to P training sample are as follows:
WhereinWithRespectively indicate input training sample be p when, the expectation of k-th of neuron node of output layer is defeated
Out and reality output.
Wherein, the number of plies of the hidden layer of neural network model is chosen using the method for minimal error.
The desired output Y of neural network are as follows:
| Lightning stroke | Mountain fire | It is filthy | Windage yaw | Bird pest |
| 0 or 1 | 0 or 1 | 0 or 1 | 0 or 1 | 0 or 1 |
Such as: when lightning stroke occurs, desired output is Y=[1 000 0], and when mountain fire occurs, desired output is Y=[0
1 0 0 0]。
But actually obtain value of the result between 0-1, such as certain identification result are as follows: [0.9 0.1 00 0].Institute
To be differentiated between practical identification result and ideal output.With overall Identification Errors EbTo characterize this species diversity:
Ebi=Rsi-Rqi (13)
Wherein i=1~5 respectively indicate lightning stroke, mountain fire, filth, windage yaw, bird pest.Rsi、RqiRespectively indicate such reason
Under reality output and desired output, EbiFor the reality output under such reason and the difference between desired output.
The range of a hidden layer is determined according to the empirical equation of hidden layer first:
In formula, m and n points of ratios are the number of nodes for outputting and inputting layer, integer of a between 0-10.Best hidden layer is really
It is as follows to determine method:
The first step calculates the value range of l, and for this method, l range is (6,16).
Second step, the implicit number of plies possible for each in l calculate Ebi、Eb。
Third step compares overall Identification Errors E corresponding to each possible implicit number of plies in lb, take the smallest Eb
The corresponding implicit number of plies is best hidden layer.It should be noted that for different training samples, the quantity of best hidden layer
It is not necessarily identical.
In step 5), the identification of failure cause is carried out using the method for self study, it is specific: when an error occurs, to utilize
The network model of creation differentiates failure cause, after troubleshooting, needs the failure that will occur as standard failure
Type typing historical failure data library, and actual failure cause and Model checking result are compared, comparing result conduct
Whether the foundation in neural metwork training library is modified.If differentiating result (using the overall Identification Errors E in step 5bTo characterize)
It is deviated, this method, which takes, works as EbWhen > 0.5, need to modify the sample set in actual failure cause model training library, being added should
The actual characteristic data of secondary failure carry out re -training, revised neural network prediction model are obtained, so as to independently learn
It practises, adapts to the variation of environment, more accurate identification of defective reason.If differentiating that result and actual result deviation are allowing model
In enclosing, does not then need to modify the sample in trained library, but record the fault signature and failure cause.Specific self study process
Such as Fig. 5.
Steps are as follows for neural metwork training:
Step 1: the formation of training sample, according to the electric characteristic amount and non-electrical characteristic quantity in step 2) and step 3)
Extraction, construct comprehensive input feature value, input vector is input 1~input 33.The failure cause class according to known to sample
Type constructs output vector, and then input vector and output vector are mapped composing training sample.
Step 2: initially taking l=6, input layer, hidden layer, output layer are successively passed through in the forward-propagating of signal.
Step 3: calculating output error, if target error meets setting value 1e-3, training output is completed as a result, no
Then carry out the 4th step.
Step 4: the backpropagation of signal successively corrects Feedback error every according to additional guide vanes trainlm
One layer of weight and threshold value.
Step 5: repeating second step, third step, the 4th step according to new weight and threshold value, set until error meets
Value.Whether l takes, and the 6th step is carried out if taking, otherwise l+1, returns to second step.
Step 6: each compared in l is possible implicit according to the determination method of the best hidden layer in step (5)
Overall Identification Errors E corresponding to the number of pliesb, take the smallest EbThe corresponding implicit number of plies is best hidden layer.
Step 7: real case is tested.After best hidden layer determines, the structure of neural network is just determined, will actual event
Barrier situation is input in neural network, the result that can be recognized.
Step 8: every time after identification, actual failure cause and prediction model are differentiated result using self-learning algorithm
It compares, whether comparing result is as modifying the foundation in neural metwork training library.If differentiating that result is deviated,
The sample set for modifying actual failure cause model training library, the actual characteristic data that the secondary failure is added carry out re -training,
Obtain revised neural network prediction model.
This method is illustrated with the case being struck by lightning: according to method and step above-mentioned, by certain to lightning stroke sample training
After obtained a neural network, with this trained neural network to test samples (test samples be not present in train sample
In this) reason identification is carried out, test samples 1-5 is caused by cause of lightning stroke, and test samples 6-10 is distracter (non-lightning stroke),
It is obtaining the result is as follows:
| Sample | Output 1 | Output 2 | Output 3 | Output 4 | Output 5 | Diagnosis of Primary because |
| 1 | 0.9859 | 0.0010 | 0.0067 | 0.0009 | 0.0104 | Lightning stroke |
| 2 | 0.9848 | 0.0018 | 0.0091 | 0.0002 | 0.0313 | Lightning stroke |
| 3 | 0.9885 | 0.0011 | 0.0018 | 0.0003 | 0.0166 | Lightning stroke |
| 4 | 0.9922 | 0.0008 | 0.0025 | 0.0016 | 0.0040 | Lightning stroke |
| 5 | 0.9949 | 0.0008 | 0.0024 | 0.0058 | 0.0250 | Lightning stroke |
| 6 | 0.0228 | 0.0007 | 0.0109 | 0.0001 | 0.3766 | Non- lightning stroke |
| 7 | 0.0328 | 0.0006 | 0.0001 | 0.0000 | 0.1732 | Non- lightning stroke |
| 8 | 0.2333 | 0.0008 | 0.0002 | 0.0000 | 0.4300 | Non- lightning stroke |
| 9 | 0.0328 | 0.0006 | 0.0001 | 0.0000 | 0.1732 | Non- lightning stroke |
| 10 | 0.0136 | 0.0035 | 0.0068 | 0.0000 | 0.4506 | Non- lightning stroke |
| Reason | Lightning stroke | Nothing | Nothing | Nothing | Nothing |
It can be seen that by the identification result of upper table Lai for test samples, neural network has carried out effective identification, right
It can effectively be identified in test samples 1-5, reach desired effect.
In conclusion the present invention is not limited to above-mentioned specific embodiments.Those skilled in the art are not departing from the present invention
Under the premise of technical solution, several changes or modification can be done, above-mentioned change or modification each fall within protection scope of the present invention.
Claims (10)
1. a kind of extra high voltage direct current transmission line failure cause discrimination method based on Multi-source Information Fusion, it is characterized in that including such as
Lower step:
The electrical quantity fault data and non-electric quantity information of step 1) acquisition extra high voltage direct current transmission line；
Step 2) extracts extra high voltage direct current transmission line electric characteristic amount, constructs electric characteristic input vector；
Step 3) extracts extra high voltage direct current transmission line non-electrical characteristic quantity, constructs non-electrical feature input vector；
Step 4) constructs global neurological network identification model, neural network model to lightning stroke, mountain fire, filth, windage yaw, bird pest respectively
Hidden layer the number of plies using minimal error method choose；
Step 5) carries out specific failure cause identification using the method for self study.
2. the extra high voltage direct current transmission line failure cause discrimination method based on Multi-source Information Fusion as described in claim 1,
It is characterized in that the electrical quantity fault data of extra high voltage direct current transmission line is obtained from protection information system, non-in step 1)
Electric quantity information is obtained from meteorological system, lightning location system, GIS-Geographic Information System, forest fire monitoring system；When the event of generation
Barrier seldom, when the lazy weight of sample, then obtains data by PSACD simulated fault.
3. the extra high voltage direct current transmission line failure cause discrimination method based on Multi-source Information Fusion as described in claim 1,
It is characterized in that in step 2), if it is t that the moment, which occurs, for failure1, extract the data after extra high voltage direct current transmission line breaks down
As electric characteristic amount, the time range of extraction is depending on operating time of protection, and generally several milliseconds to a few tens of milliseconds.It is electrical special
Sign amount includes fault current maximum amplitude, the feature of voltage and current amplitude frequency diagram, transition resistance mean value, transition resistance standard deviation etc.；
Fault current maximum amplitude is | I |；Wherein electric current I is the value of fault current, is positive or is negative；
The feature of voltage and current amplitude frequency diagram needs to carry out discrete fourier to data to decompose to obtain, according to formula
To Current Voltage Fourier transformation, the waveform of frequency domain is obtained, and then extracts and obtains the characteristic quantity of voltage and current amplitude frequency diagram；
According to formula
Obtain transition resistance mean value；
According to formula
Transition resistance standard deviation E；
Establish electric characteristic input vector.
4. the extra high voltage direct current transmission line failure cause discrimination method based on Multi-source Information Fusion as described in claim 1,
It is characterized in that in step 3), non-electrical characteristic quantity is divided into the weather, period, season, landform, lightening activity frequency, temperature, wet
Degree, 9 characteristic quantities of wind-force and historical failure information, indicated by orderly binary number, establish non-electrical feature input to
Amount.
5. the extra high voltage direct current transmission line failure cause discrimination method based on Multi-source Information Fusion as described in claim 1,
It is characterized in that using three layers of BP neural network structure, input layer number is 33, and hidden layer uses minimal error in step 4)
Method choose, output layer number of nodes be 5；The activation primitive of hidden layer and output layer is all made of log-sigmoid type function, can
Control will be exported within the scope of 0-1；Training method uses trainlm algorithm, and training objective error is set as 1e-4, training
Number is set as 1000 times.
6. the extra high voltage direct current transmission line failure cause discrimination method based on Multi-source Information Fusion as claimed in claim 5,
It is characterized in that BP neural network is according to the multilayer feedforward neural network of error backpropagation algorithm training, if input layer includes N
A neuron, output layer include M neuron, and input vector X, output vector Y, hidden layer includes L neuron node,
wijIndicate input layer to the connection weight between hidden layer, wjkIndicate hidden layer to the connection weight between output layer, θjIt indicates
The threshold value of hidden layer neuron, θkIndicate the threshold value of output layer neuron,Indicate the activation primitive of hidden layer, φ ()
Indicate the activation primitive of output layer.
The logarithmic function of S type are as follows:
The output signal o of j-th of neuron node of hidden layerjAre as follows:
The output signal y of k-th of neuron node of output layerkAre as follows:
Equipped with P training sample, for each training sample, the quadratic form criterion function of error are as follows:
Global error function of the network to P training sample are as follows:
WhereinWithRespectively indicate input training sample be p when, the desired output of k-th of neuron node of output layer and
Reality output.
7. the extra high voltage direct current transmission line failure cause discrimination method based on Multi-source Information Fusion as claimed in claim 6,
It is characterized in that being to have difference between practical identification result and ideal output, with overall Identification Errors EbTo characterize this species diversity:
Ebi=Rsi-Rqi (11)
Wherein i=1~5 respectively indicate lightning stroke, mountain fire, filth, windage yaw, bird pest；Rsi、RqiRespectively indicate the reality under such reason
Border output and desired output, EbiFor the reality output under such reason and the difference between desired output.
8. the extra high voltage direct current transmission line failure cause discrimination method based on Multi-source Information Fusion as claimed in claim 7,
It is characterized in that determining the range of a hidden layer according to the empirical equation of hidden layer:
In formula, m and n are respectively the number of nodes for outputting and inputting layer, integer of a between 0-10；
The determination step of best hidden layer are as follows:
Step 4.1) calculates the value range of l, and the range of l is (6,16)；
Step 4.2) the implicit number of plies possible for each in l calculates Ebi、Eb；
Step 4.3) compares overall Identification Errors E corresponding to each possible implicit number of plies in lb, take the smallest EbInstitute is right
The implicit number of plies answered is best hidden layer.
9. the extra high voltage direct current transmission line failure cause discrimination method based on Multi-source Information Fusion as claimed in claim 7,
It is characterized in that when failure occurs, being differentiated using the network model of creation to failure cause, troubleshooting knot in step 5)
Shu Hou, using the failure of generation as standard failure type typing historical failure data library, and by actual failure cause and model
Differentiate that result compares, whether comparing result is as modifying the foundation in neural metwork training library；As overall Identification Errors Eb>
When 0.5, the sample set in actual failure cause model training library is modified, the actual characteristic data that the secondary failure is added carry out again
Training, obtains revised neural network prediction model, so as to autonomous learning, adapts to the variation of environment, more accurately
Identification of defective reason；If differentiating result and actual result deviation within the allowable range, do not need to modify the sample in trained library
This, but record the fault signature and failure cause.
10. the extra high voltage direct current transmission line failure cause discrimination method based on Multi-source Information Fusion as claimed in claim 9,
It is characterized in that the step of neural metwork training are as follows:
The formation of step 5.1) training sample, according in step 2) and step 3) electric characteristic amount and non-electrical characteristic quantity mention
It takes, constructs comprehensive input feature value, input vector is input 1~input 33, the Fault Cause Type structure according to known to sample
It makes output vector, and then input vector and output vector is mapped composing training sample；
Step 5.2) initially takes l=6, and input layer, hidden layer, output layer are successively passed through in the forward-propagating of signal；
Step 5.3) calculate output error, if target error meets setting value 1e-3, complete training output as a result, otherwise into
The 4th step of row；
Feedback error is successively corrected each layer according to additional guide vanes trainlm by the backpropagation of step 5.4) signal
Weight and threshold value；
Step 5.5) repeats step 5.2), step 5.3), step 5.4) according to new weight and threshold value, sets until error meets
Value；Judge whether l takes, step 5.6) is carried out if taking, otherwise l+1, return step 5.2)；
Step 5.6) compares total corresponding to each possible implicit number of plies in l according to the determination method of best hidden layer
Body Identification Errors Eb, take the smallest EbThe corresponding implicit number of plies is best hidden layer；
Step 5.7) carries out real case test, and after best hidden layer determines, the structure of neural network is just determined, will actual event
Barrier situation is input in neural network, the result recognized；
Step 5.8) uses self-learning algorithm, and every time after identification, actual failure cause and prediction model are differentiated that result carries out
Comparison, whether comparing result is as modifying the foundation in neural metwork training library；If differentiating that result is deviated, modification is real
The sample set in the failure cause model training library on border, the actual characteristic data that the secondary failure is added carry out re -training, are repaired
Neural network prediction model after just.
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