CN106778692A - A kind of cable local discharge signal recognition method and device based on S-transformation - Google Patents

Abstract

The embodiment of the invention discloses a kind of cable local discharge signal recognition method and device based on S-transformation, for solving at present to the positioning in power cable partial discharge signal source generally using waveform time difference method, the technical problem that accuracy of identification is low and required recognition time is long.Present invention method includes：The local discharge signal in the known source to getting carries out S-transformation, obtains multiple time-frequency matrix；Modulus are carried out to multiple time-frequency matrix and obtains modular matrix, and singular value decomposition is carried out to modular matrix, obtain the unusual value sequence of modular matrix；Unusual value sequence is divided at least two interval, the Shannon entropys of singular value in each interval and the ratio of the Shannon entropys of unusual value sequence are calculated, and is set up local discharge signal feature samples storehouse using ratio as local discharge signal characteristic vector and is built supporting vector machine model；The characteristic vector of local discharge signal to be identified is input to supporting vector machine model, the source of local discharge signal to be identified is obtained.

Description

A kind of cable local discharge signal recognition method and device based on S-transformation

Technical field

The present invention relates to cable local discharge on-line monitoring technique field, more particularly to a kind of cable office based on S-transformation Portion's discharge signal recognition methods and device.

Background technology

With the high speed sustainable development of power system, the loop length of laying of power cable is steadily improved, in city Extensive use is arrived.However, electric load is growing with voltage class, by Insulation Problems caused by cable local defect to supplying Electricity quality and social economy etc. constitute greatly threat.In order to monitor the state of insulation of cable and find that it locally lacks in time Fall into, so as to prevent the generation of cable interruption of service, it is ensured that the reliability of operation of power networks, it is necessary to which cable local defect is examined Survey.

In cable local discharge on-line monitoring, the local discharge signal for detecting may be from cable body and cable termination Head, it is also possible to from coupled switch cubicle.Because the shelf depreciation of separate sources endangers different to equipment, criterion Difference, so being identified important realistic meaning to local discharge signal source.

In terms of local discharge signal identification, signal characteristic abstraction and grader selection are most critical parts.Feature extraction It is the local discharge signal identification first step, the quality of feature extraction directly influences the effect of identification.At present, local discharge signal Feature extracting method mainly has statistical nature method and the major class of temporal analysis two.Wherein statistical nature method is directed to shelf depreciation The phase of signal, and distribution cable is generally three-core cable and totally one ground wire, when there is shelf depreciation in two-phase or three-phase, detection The phase property of local discharge signal becomes hardly possible.Temporal analysis be directed to high speed acquisition once discharge generation when Wave character or corresponding transformation results obtained by the pulse of domain carry out pattern-recognition, mainly including Fourier analysis method, small echo Analytic approach and waveform parameter direct extraction method etc..Pattern recognition classifier device mainly has neural network classifier, minimum distance classification Device and fuzzy diagnosis grader.Neutral net easily converges on locally optimal solution defect, and precision is not high.

At present, whole power industry generally uses waveform time difference side to the positioning in power cable partial discharge signal source Method, through be commonly present for transmit the power cable line of the local discharge signal it is long when, accuracy of identification is substantially reduced and required Recognition time is long.

The content of the invention

A kind of cable local discharge signal recognition method and device based on S-transformation are the embodiment of the invention provides, is solved Current power industry generally uses waveform time difference method to the positioning in power cable partial discharge signal source, through being commonly present use When the power cable line for transmitting the local discharge signal is long, accuracy of identification is substantially reduced and required recognition time is long Technical problem.

A kind of cable local discharge signal recognition method based on S-transformation provided in an embodiment of the present invention, including：

The local discharge signal in known source is obtained, and S-transformation is carried out to local discharge signal, obtain multiple time-frequency matrix；

Modulus are carried out to multiple time-frequency matrix and obtains modular matrix, and singular value decomposition is carried out to modular matrix, obtain modular matrix Unusual value sequence；

Unusual value sequence is divided into according to unusual value sequence at least two interval, calculates singular value in each interval The ratio of Shannon entropys and the Shannon entropys of unusual value sequence, and using ratio as local discharge signal characteristic vector foundation office Portion discharge signal feature samples storehouse；

Using the local discharge signal characteristic vector in local discharge signal feature samples storehouse as input, many classification branch are built Hold vector machine model；

Using local discharge signal as sample, supporting vector machine model is trained, the supporting vector for being trained Machine model；

The characteristic vector of local discharge signal to be identified is input to the supporting vector machine model for training, obtains waiting to know The source of other local discharge signal.

Alternatively, local discharge signal includes cable body local discharge signal, cable terminal local discharge signal, opens Close the corona discharge signal of cabinet and the surface-discharge signal of switch cubicle.

Alternatively, the local discharge signal in known source is obtained, and S-transformation is carried out to local discharge signal, when obtaining multiple Frequency matrix includes：

The local discharge signal in known source is obtained, and S-transformation is carried out by a pair of local discharge signals of preset formula, obtained To multiple time-frequency matrix, preset formula one is specially：

Wherein, h (kT) is local discharge signal discrete-time series, and ST passes through for local discharge signal discrete-time series The multiple time-frequency matrix obtained after S-transformation, T for discrete-time series sampling period, N for discrete-time series length, H be from The Fourier transform of time series is dissipated, j is imaginary unit, k, n, m=0,1 ..., N-1.

Alternatively, modulus are carried out to multiple time-frequency matrix and obtains modular matrix, and singular value decomposition is carried out to modular matrix, obtain mould The unusual value sequence of matrix includes：

Modulus are carried out to multiple time-frequency matrix ST and obtains modular matrix STA, and modular matrix STA is carried out by preset second formula Singular value decomposition, obtains the unusual value sequence of modular matrix, and preset second formula is specially：

Wherein, U and V are N × N rank orthogonal matrixes, D=diag (σ₁,σ₂,…,σ_N) it is diagonal matrix, its diagonal element (σ₁,σ₂,…,σ_N) it is the singular value of matrix STA, u_iAnd v_iRespectively the i-th row singular value vector of matrix U and V.

Alternatively, unusual value sequence is divided into by least two intervals according to unusual value sequence, is calculated in each interval The ratio of the Shannon entropys of singular value and the Shannon entropys of unusual value sequence, and using ratio as local discharge signal feature to Amount sets up local discharge signal feature samples storehouse to be included：

Unusual value sequence is divided into according to unusual value sequence at least two interval, calculates singular value in each interval The ratio of Shannon entropys and the Shannon entropys of unusual value sequence, and believed as shelf depreciation using ratio by preset 3rd formula Number characteristic vector sets up local discharge signal feature samples storehouse, and preset 3rd formula is specially：

λ=[E₁/E,E₂/E,E_q/E…,E_Q/E]；

Wherein,It is unusual value sequence（σ₁, σ₂..., σ_N) Shannon entropys, For singular value obtains Shannon entropys, σ in the q of minizone_iQ () is the singular value in the q of minizone, Q is minizone number.

Alternatively, the source categories of local discharge signal characteristic vector are marked using two bits.

Alternatively, using the local discharge signal characteristic vector in local discharge signal feature samples storehouse as input, build Multi-category support vector machines model includes：

With two sorting algorithms by the source of the local discharge signal characteristic vector in local discharge signal feature samples storehouse Each source categories in classification are combined, and constitute a plurality of sub-classifiers, and with local discharge signal feature samples storehouse Local discharge signal characteristic vector as input, build multi-category support vector machines model.

Alternatively, using local discharge signal as sample, supporting vector machine model is trained, the branch for being trained Holding vector machine model includes：

The local discharge signal that separate sources according to local discharge signal selects the separate sources of equal number at random is made It is training sample, and training sample is input into supporting vector machine model to be trained, the SVMs mould for being trained Type.

Alternatively, the characteristic vector of local discharge signal to be identified is input to the supporting vector machine model for training, The source for obtaining local discharge signal to be identified includes：

The characteristic vector of local discharge signal to be identified is input to the supporting vector machine model for training to be exported Value, and output valve is contrasted with the binary number mark of the source categories of local discharge signal characteristic vector, obtain waiting to know The source of other local discharge signal.

A kind of cable local discharge signal identification device based on S-transformation provided in an embodiment of the present invention, it is characterised in that Including：

Conversion module, for obtaining the local discharge signal in known source, and carries out S-transformation to local discharge signal, obtains To multiple time-frequency matrix；

Decomposing module, modular matrix is obtained for carrying out modulus to multiple time-frequency matrix, and carries out singular value decomposition to modular matrix, Obtain the unusual value sequence of modular matrix；

Computing module, for unusual value sequence to be divided into at least two intervals according to unusual value sequence, calculates each area The ratio of the Shannon entropys of interior singular value and the Shannon entropys of unusual value sequence, and using ratio as local discharge signal Characteristic vector sets up local discharge signal feature samples storehouse；

Module is built, for using the local discharge signal characteristic vector in local discharge signal feature samples storehouse as defeated Enter, build multi-category support vector machines model；

Training module, for using local discharge signal as sample, being trained to supporting vector machine model, is trained Good supporting vector machine model；

Input module, for the characteristic vector of local discharge signal to be identified to be input into the SVMs for training Model, obtains the source of local discharge signal to be identified.

As can be seen from the above technical solutions, the embodiment of the present invention has advantages below：

A kind of cable local discharge signal recognition method and device based on S-transformation are the embodiment of the invention provides, including： The local discharge signal in known source is obtained, and S-transformation is carried out to local discharge signal, obtain multiple time-frequency matrix；To multiple time-frequency Matrix carries out modulus and obtains modular matrix, and carries out singular value decomposition to modular matrix, obtains the unusual value sequence of modular matrix；According to strange Different value sequence unusual value sequence is divided into it is at least two interval, calculate the Shannon entropys of singular value in each interval with it is strange The ratio of the Shannon entropys of different value sequence, and it is special to set up local discharge signal using ratio as local discharge signal characteristic vector Levy Sample Storehouse；Using the local discharge signal characteristic vector in local discharge signal feature samples storehouse as input, many classification are built Supporting vector machine model；Using local discharge signal as sample, supporting vector machine model is trained, the branch for being trained Hold vector machine model；The characteristic vector of local discharge signal to be identified is input to the supporting vector machine model for training, is obtained To the source of local discharge signal to be identified, in the embodiment of the present invention after carrying out S-transformation to local discharge signal, obtain Multiple time-frequency matrix；Modulus are carried out to multiple time-frequency matrix again and obtains modular matrix, and singular value decomposition is carried out to modular matrix, obtain mould square The unusual value sequence of battle array；Unusual value sequence is divided into by least two intervals according to unusual value sequence, is calculated in each interval The ratio of the Shannon entropys of singular value and the Shannon entropys of unusual value sequence, and using ratio as local discharge signal feature to Amount sets up local discharge signal feature samples storehouse；With the local discharge signal characteristic vector in local discharge signal feature samples storehouse As input, multi-category support vector machines model is built, as long as final that the characteristic vector of local discharge signal to be identified is defeated Enter to the supporting vector machine model for training, you can the source of local discharge signal to be identified is got, with identification step Simply, the characteristics of accuracy of identification is high, recognition speed is fast, solves current power industry to power cable partial discharge signal source Positioning generally uses waveform time difference method, long for transmitting the power cable line of the local discharge signal through being commonly present When, accuracy of identification is substantially reduced and the long technical problem of required recognition time.

Brief description of the drawings

In order to illustrate more clearly about the embodiment of the present invention or technical scheme of the prior art, below will be to embodiment or existing The accompanying drawing to be used needed for having technology description is briefly described, it should be apparent that, drawings in the following description are only this Some embodiments of invention, for those of ordinary skill in the art, without having to pay creative labor, may be used also Other accompanying drawings are obtained with according to these accompanying drawings.

Fig. 1 is of a kind of cable local discharge signal recognition method based on S-transformation provided in an embodiment of the present invention The schematic flow sheet of embodiment；

Fig. 2 is cable body local discharge signal oscillogram provided in an embodiment of the present invention；

Fig. 3 is cable terminal local discharge signal oscillogram provided in an embodiment of the present invention；

Fig. 4 is the corona discharge signal of switch cubicle provided in an embodiment of the present invention；

Fig. 5 is the surface-discharge signal waveforms of switch cubicle provided in an embodiment of the present invention；

Fig. 6 is a kind of the another of cable local discharge signal recognition method based on S-transformation provided in an embodiment of the present invention The schematic flow sheet of individual embodiment；

Fig. 7 is a kind of structure of cable local discharge signal identification device based on S-transformation provided in an embodiment of the present invention Schematic diagram.

Specific embodiment

A kind of cable local discharge signal recognition method and device based on S-transformation are the embodiment of the invention provides, is used for Current power industry is solved to the positioning in power cable partial discharge signal source generally using waveform time difference method, through being commonly present When power cable line for transmitting the local discharge signal is long, accuracy of identification is substantially reduced and required recognition time is long Technical problem.

To enable that goal of the invention of the invention, feature, advantage are more obvious and understandable, below in conjunction with the present invention Accompanying drawing in embodiment, is clearly and completely described, it is clear that disclosed below to the technical scheme in the embodiment of the present invention Embodiment be only a part of embodiment of the invention, and not all embodiment.Based on the embodiment in the present invention, this area All other embodiment that those of ordinary skill is obtained under the premise of creative work is not made, belongs to protection of the present invention Scope.

Fig. 1 is referred to, a kind of cable local discharge signal recognition method based on S-transformation provided in an embodiment of the present invention One embodiment includes：

101st, the local discharge signal in known source is obtained, and S-transformation is carried out to local discharge signal, obtain multiple time-frequency square Battle array；

First, the local discharge signal of cable is got, wherein the office of separate sources can be got by corresponding experiment Portion's discharge signal, the local discharge signal of separate sources includes cable body local discharge signal, cable terminal shelf depreciation Surface-discharge signal of signal, the corona discharge signal of switch cubicle and switch cubicle etc., as shown in Fig. 2 for cable body is locally put Electric signal waveform figure；As shown in figure 3, being cable terminal local discharge signal oscillogram；As shown in figure 4, being the electricity of switch cubicle Corona signal；As shown in figure 5, being the surface-discharge signal waveforms of switch cubicle.Local discharge signal to getting carries out S Conversion, you can obtain corresponding multiple time-frequency matrix.

102nd, modulus are carried out to multiple time-frequency matrix and obtains modular matrix, and singular value decomposition is carried out to modular matrix, obtain mould square The unusual value sequence of battle array；

In the local discharge signal in the known source of acquisition, and S-transformation is carried out to local discharge signal, obtain multiple time-frequency matrix Afterwards, modulus are carried out to multiple time-frequency matrix and obtains modular matrix, and singular value decomposition is carried out to modular matrix, obtain the unusual of modular matrix Value sequence.

103rd, unusual value sequence is divided into by least two intervals according to unusual value sequence, calculates unusual in each interval The ratio of the Shannon entropys of value and the Shannon entropys of unusual value sequence, and built as local discharge signal characteristic vector using ratio Vertical local discharge signal feature samples storehouse；

Modular matrix is obtained modulus are carried out to multiple time-frequency matrix, and singular value decomposition is carried out to modular matrix, obtain modular matrix Unusual value sequence after, unusual value sequence is divided into according to unusual value sequence it is at least two interval, such as by unusual value sequence 10 minizones are divided into, the Shannon entropys of singular value in each minizone and the Shannon of whole unusual value sequence is calculated The ratio of entropy, and local discharge signal feature samples storehouse is set up as local discharge signal characteristic vector using ratio.

It should be noted that when local discharge signal feature samples storehouse is set up, can be marked using two bits The source categories of local discharge signal characteristic vector, that is, mark the separate sources of local discharge signal, such as cable local discharge letter Number, the surface-discharge signal in corona discharge signal and switch cubicle in cable terminal local discharge signal, switch cubicle can divide Not Biao Ji for：(+1 ,+1), (+1, -1), (- 1 ,+1), (- 1, -1).

104th, using the local discharge signal characteristic vector in local discharge signal feature samples storehouse as input, many points are built Class support vector machines model；

Unusual value sequence is being divided into by least two intervals according to unusual value sequence, the singular value in each interval is being calculated Shannon entropys and the Shannon entropys of unusual value sequence ratio, and set up as local discharge signal characteristic vector using ratio After local discharge signal feature samples storehouse, made with the local discharge signal characteristic vector in local discharge signal feature samples storehouse It is input, builds multi-category support vector machines model.

105th, using local discharge signal as sample, supporting vector machine model is trained, the support for being trained Vector machine model；

After multi-category support vector machines model construction is finished, by getting the local discharge signal of separate sources and inciting somebody to action The local discharge signal of the separate sources for getting is trained as sample in input to supporting vector machine model, can obtain To the supporting vector machine model for training.

106th, the characteristic vector of local discharge signal to be identified is input to the supporting vector machine model for training, is obtained The source of local discharge signal to be identified.

Finally, when needing to be identified the source of the local discharge signal of cable, shelf depreciation to be identified is believed Number the characteristic vector for obtaining local discharge signal is calculated, and the characteristic vector of local discharge signal is input to the support for training Vector machine model, the output valve according to supporting vector machine model is the source of the local discharge signal that can obtain to be identified.

It is more than to a kind of cable local discharge signal recognition method based on S-transformation provided in an embodiment of the present invention The detailed description of individual embodiment, below by a kind of cable local discharge signal based on S-transformation provided in an embodiment of the present invention Another embodiment of recognition methods is described in detail.

Fig. 6 is referred to, a kind of cable local discharge signal recognition method based on S-transformation provided in an embodiment of the present invention Another embodiment includes：

201st, the local discharge signal in known source is obtained, and S changes is carried out by a pair of local discharge signals of preset formula Change, obtain multiple time-frequency matrix；

First, the local discharge signal of cable is got, wherein the office of separate sources can be got by corresponding experiment Portion's discharge signal, the local discharge signal of separate sources includes cable body local discharge signal, cable terminal shelf depreciation Surface-discharge signal of signal, the corona discharge signal of switch cubicle and switch cubicle etc., as shown in Fig. 2 for cable body is locally put Electric signal waveform figure；As shown in figure 3, being cable terminal local discharge signal oscillogram；As shown in figure 4, being the electricity of switch cubicle Corona signal；As shown in figure 5, being the surface-discharge signal waveforms of switch cubicle.Local discharge signal to getting passes through Preset formula one carries out S-transformation, you can obtain corresponding multiple time-frequency matrix, and preset formula one is specially：

Wherein, h (kT) is local discharge signal discrete-time series, and ST passes through for local discharge signal discrete-time series The multiple time-frequency matrix obtained after S-transformation, T for discrete-time series sampling period, N for discrete-time series length, H be from The Fourier transform of time series is dissipated, j is imaginary unit, k, n, m=0,1 ..., N-1.

202nd, modulus are carried out to multiple time-frequency matrix ST and obtains modular matrix STA, and by preset second formula to modular matrix STA Singular value decomposition is carried out, the unusual value sequence of modular matrix is obtained；

In the local discharge signal in the known source of acquisition, and S-transformation is carried out by a pair of local discharge signals of preset formula, Obtain after multiple time-frequency matrix, modulus are carried out to multiple time-frequency matrix ST and obtains modular matrix STA, and by preset second formula to mould Matrix STA carries out singular value decomposition, obtains the unusual value sequence of modular matrix, and preset second formula is specially：

Wherein, U and V are N × N rank orthogonal matrixes, D=diag (σ₁,σ₂,…,σ_N) it is diagonal matrix, its diagonal element (σ₁,σ₂,…,σ_N) it is the singular value of matrix STA, u_iAnd v_iRespectively the i-th row singular value vector of matrix U and V.

203rd, unusual value sequence is divided into by least two intervals according to unusual value sequence, calculates unusual in each interval The ratio of the Shannon entropys of value and the Shannon entropys of unusual value sequence, and put as local using ratio by preset 3rd formula Signal characteristics vector sets up local discharge signal feature samples storehouse；

Modular matrix STA is obtained modulus are carried out to multiple time-frequency matrix ST, and modular matrix STA is entered by preset second formula Row singular value decomposition, obtains after the unusual value sequence of modular matrix, is divided at least unusual value sequence according to unusual value sequence Two intervals, calculate the Shannon entropys of singular value in each interval and the ratio of the Shannon entropys of unusual value sequence, and lead to Cross preset 3rd formula and local discharge signal feature samples storehouse, preset are set up as local discharge signal characteristic vector using ratio Three formula are specially：

λ=[E₁/E,E₂/E,E_q/E…,E_Q/E]；

Wherein,It is unusual value sequence（σ₁, σ₂..., σ_N) Shannon entropys, For singular value obtains Shannon entropys, σ in the q of minizone_iQ () is the singular value in the q of minizone, Q is minizone number.

It should be noted that when local discharge signal feature samples storehouse is set up, can be marked using two bits The source categories of local discharge signal characteristic vector, that is, mark the separate sources of local discharge signal, such as cable local discharge letter Number, the surface-discharge signal in corona discharge signal and switch cubicle in cable terminal local discharge signal, switch cubicle can divide Not Biao Ji for：(+1 ,+1), (+1, -1), (- 1 ,+1), (- 1, -1).

204th, with two sorting algorithms by the local discharge signal characteristic vector in local discharge signal feature samples storehouse Each source categories in source categories are combined, and constitute a plurality of sub-classifiers, and with local discharge signal feature samples Local discharge signal characteristic vector in storehouse builds multi-category support vector machines model as input；

Unusual value sequence is being divided into by least two intervals according to unusual value sequence, the singular value in each interval is being calculated Shannon entropys and the Shannon entropys of unusual value sequence ratio, and by preset 3rd formula using ratio as shelf depreciation Signal characteristic vector is set up after local discharge signal feature samples storehouse, with two sorting algorithms by local discharge signal feature sample Each source categories in the source categories of the local discharge signal characteristic vector of Ben Kunei are combined, and constitute plural height point Class device, and using the local discharge signal characteristic vector in local discharge signal feature samples storehouse as input, build many classification branch Hold vector machine model.Because common supporting vector machine model is two category support vector machines models, and the part of required identification Discharge signal source categories have four kinds, it is therefore desirable to by using two sorting algorithms by local discharge signal feature samples storehouse Each source categories in the source categories of local discharge signal characteristic vector are combined, and may be constructed 2 sub-classifiers SVM1 and SVM2, many classification are expanded to by the classification of SVMs two.

If with A, B, C, D represent cable body local discharge signal, cable terminal local discharge signal, open respectively The 4 kinds of discharge signals of surface-discharge signal in the corona discharge signal and switch cubicle in cabinet are closed, then SVM1 and SVM2 output results It is as shown in table 1 with discharge signal type corresponding relation.

Table 1

205th, the separate sources according to local discharge signal selects the shelf depreciation letter of the separate sources of equal number at random Number as training sample, and training sample is input to supporting vector machine model it is trained, the supporting vector for being trained Machine model；

After multi-category support vector machines model construction is finished, chosen at random by the separate sources according to local discharge signal Select the local discharge signal of separate sources of equal number as training sample, i.e., selected at random from four kinds of local discharge signals The local discharge signal of equal number, such as 50 cable body local discharge signals, 50 cable terminal local discharge signals, The surface-discharge signal of 50 corona discharge signals of switch cubicle, 50 switch cubicles, and training sample is input to supporting vector Machine model is trained, the supporting vector machine model for being trained.

206th, the characteristic vector of local discharge signal to be identified is input to the supporting vector machine model for training to obtain Output valve, and output valve is contrasted with the binary number mark of the source categories of local discharge signal characteristic vector, obtain The source of local discharge signal to be identified.

Finally, when needing to be identified the source of the local discharge signal of cable, shelf depreciation to be identified is believed Number the characteristic vector for obtaining local discharge signal is calculated, and the characteristic vector of local discharge signal to be identified is input to training Good supporting vector machine model obtains output valve, and output valve is entered with the two of the source categories of local discharge signal characteristic vector Number mark processed is contrasted, and obtains the source of local discharge signal to be identified, i.e., as shown in table 1, output valve is (+1 ,+1), (+1, -1), (- 1 ,+1), (- 1, -1) corresponds to cable local discharge signal, cable terminal local discharge signal, switch cubicle respectively In corona discharge signal, switch cubicle in surface-discharge signal.

It is more than to a kind of the another of cable local discharge signal recognition method based on S-transformation provided in an embodiment of the present invention The detailed description of one embodiment, below by a kind of cable local discharge letter based on S-transformation provided in an embodiment of the present invention Number identifying device is described in detail.

Refer to Fig. 7, a kind of cable local discharge signal identification device bag based on S-transformation provided in an embodiment of the present invention Include：

Conversion module 301, for obtaining the local discharge signal in known source, and carries out S-transformation to local discharge signal, Obtain multiple time-frequency matrix；

Decomposing module 302, modular matrix is obtained for carrying out modulus to multiple time-frequency matrix, and singular value point is carried out to modular matrix Solution, obtains the unusual value sequence of modular matrix；

Computing module 303, for unusual value sequence to be divided into at least two intervals according to unusual value sequence, calculates each The ratio of the Shannon entropys of the singular value in interval and the Shannon entropys of unusual value sequence, and believed as shelf depreciation using ratio Number characteristic vector sets up local discharge signal feature samples storehouse；

Build module 304, for using the local discharge signal characteristic vector in local discharge signal feature samples storehouse as Input, builds multi-category support vector machines model；

Training module 305, for using local discharge signal as sample, being trained to supporting vector machine model, obtains The supporting vector machine model for training；

Input module 306, for by the characteristic vector of local discharge signal to be identified be input to the support for training to Amount machine model, obtains the source of local discharge signal to be identified.

It is apparent to those skilled in the art that, for convenience and simplicity of description, the system of foregoing description, The specific work process of device and unit, may be referred to the corresponding process in preceding method embodiment, will not be repeated here.

In several embodiments provided herein, it should be understood that disclosed system, apparatus and method can be with Realize by another way.For example, device embodiment described above is only schematical, for example, the unit Divide, only a kind of division of logic function there can be other dividing mode when actually realizing, for example multiple units or component Can combine or be desirably integrated into another system, or some features can be ignored, or do not perform.It is another, it is shown or The coupling each other for discussing or direct-coupling or communication connection can be the indirect couplings of device or unit by some interfaces Close or communicate to connect, can be electrical, mechanical or other forms.

The unit that is illustrated as separating component can be or may not be it is physically separate, it is aobvious as unit The part for showing can be or may not be physical location, you can with positioned at a place, or can also be distributed to multiple On NE.Some or all of unit therein can be according to the actual needs selected to realize the mesh of this embodiment scheme 's.

In addition, during each functional unit in each embodiment of the invention can be integrated in a processing unit, it is also possible to It is that unit is individually physically present, it is also possible to which two or more units are integrated in a unit.Above-mentioned integrated list Unit can both be realized in the form of hardware, it would however also be possible to employ the form of SFU software functional unit is realized.

If the integrated unit is to realize in the form of SFU software functional unit and as independent production marketing or use When, can store in a computer read/write memory medium.Based on such understanding, technical scheme is substantially The part for being contributed to prior art in other words or all or part of the technical scheme can be in the form of software products Embody, the computer software product is stored in a storage medium, including some instructions are used to so that a computer Equipment (can be personal computer, server, or network equipment etc.) performs the complete of each embodiment methods described of the invention Portion or part steps.And foregoing storage medium includes：USB flash disk, mobile hard disk, read-only storage (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), magnetic disc or CD etc. are various can store journey The medium of sequence code.

The above, the above embodiments are merely illustrative of the technical solutions of the present invention, rather than its limitations；Although with reference to preceding Embodiment is stated to be described in detail the present invention, it will be understood by those within the art that：It still can be to preceding State the technical scheme described in each embodiment to modify, or equivalent is carried out to which part technical characteristic；And these Modification is replaced, and does not make the spirit and scope of the essence disengaging various embodiments of the present invention technical scheme of appropriate technical solution.

Claims (10)

1. a kind of cable local discharge signal recognition method based on S-transformation, it is characterised in that including：

The local discharge signal in known source is obtained, and S-transformation is carried out to the local discharge signal, obtain multiple time-frequency matrix；

Modulus are carried out to the multiple time-frequency matrix and obtains modular matrix, and singular value decomposition is carried out to the modular matrix, obtain described The unusual value sequence of modular matrix；

The unusual value sequence is divided into by least two intervals according to the unusual value sequence, calculates unusual in each interval The ratio of the Shannon entropys of value and the Shannon entropys of the unusual value sequence, and it is special as local discharge signal using the ratio Levy vector and set up local discharge signal feature samples storehouse；

Using the local discharge signal characteristic vector in the local discharge signal feature samples storehouse as input, many classification branch are built Hold vector machine model；

Using the local discharge signal as sample, the supporting vector machine model is trained, the support for being trained Vector machine model；

The characteristic vector of local discharge signal to be identified is input to the supporting vector machine model for training, obtains waiting to know The source of other local discharge signal.

2. the cable local discharge signal recognition method based on S-transformation according to claim 1, it is characterised in that described Local discharge signal includes cable body local discharge signal, cable terminal local discharge signal, the corona discharge of switch cubicle The surface-discharge signal of signal and switch cubicle.

3. the cable local discharge signal recognition method based on S-transformation according to claim 1, it is characterised in that described The local discharge signal in known source is obtained, and S-transformation is carried out to the local discharge signal, obtaining multiple time-frequency matrix includes：

Obtain the local discharge signal in known source, and S-transformation is carried out by local discharge signal described in a pair of preset formula, obtain To multiple time-frequency matrix, the preset formula one is specially：

$ST(kT,\frac{n}{NT})=\left\{\begin{array}{c}\underset{m=0}{\overset{N-1}{\Σ}}H\left(\frac{m+n}{NT}\right)e^{-\frac{2{\mathrm{\π}}^2{m}^2}{n^2}}{e}^{j\frac{2\mathrm{\π}mk}{N}},n\≠0\\ \frac{1}{N}\underset{k=0}{\overset{N-1}{\Σ}}h\left(kT\right),n=0\end{array}\right.;$

Wherein, h (kT) is local discharge signal discrete-time series, and ST is that local discharge signal discrete-time series becomes by S The multiple time-frequency matrix obtained after changing, T is the sampling period of discrete-time series, and N is the length of discrete-time series, and H is discrete The Fourier transform of time series, j is imaginary unit, k, n, m=0,1 ..., N-1.

4. the cable local discharge signal recognition method based on S-transformation according to claim 3, it is characterised in that described Modulus are carried out to the multiple time-frequency matrix and obtains modular matrix, and singular value decomposition is carried out to the modular matrix, obtain the mould square The unusual value sequence of battle array includes：

Modulus are carried out to the multiple time-frequency matrix ST and obtains modular matrix STA, and by preset second formula to the modular matrix STA Singular value decomposition is carried out, the unusual value sequence of the modular matrix is obtained, preset second formula is specially：

$STA={\mathrm{UDV}}^T=\underset{i=1}{\overset{r}{\Σ}}{\mathrm{\σ}}_i{u}_i{v}_i^T;$

Wherein, U and V are N × N rank orthogonal matrixes, D=diag (σ₁,σ₂,…,σ_N) it is diagonal matrix, its diagonal element (σ₁, σ₂,…,σ_N) it is the singular value of matrix STA, u_iAnd v_iRespectively the i-th row singular value vector of matrix U and V.

5. the cable local discharge signal recognition method based on S-transformation according to claim 4, it is characterised in that described The unusual value sequence is divided into according to the unusual value sequence at least two interval, calculates singular value in each interval The ratio of Shannon entropys and the Shannon entropys of the unusual value sequence, and using the ratio as local discharge signal feature to Amount sets up local discharge signal feature samples storehouse to be included：

The unusual value sequence is divided into by least two intervals according to the unusual value sequence, calculates unusual in each interval The ratio of the Shannon entropys of value and the Shannon entropys of the unusual value sequence, and made with the ratio by preset 3rd formula For local discharge signal characteristic vector sets up local discharge signal feature samples storehouse, preset 3rd formula is specially：

λ=[E₁/E,E₂/E,E_q/E…,E_Q/E]；

Singular value, Q be minizone number.

6. the cable local discharge signal recognition method based on S-transformation according to claim 5, it is characterised in that use Two bits mark the source categories of the local discharge signal characteristic vector.

7. the cable local discharge signal recognition method based on S-transformation according to claim 6, it is characterised in that described Using the local discharge signal characteristic vector in the local discharge signal feature samples storehouse as input, build many classification support to Amount machine model includes：

With two sorting algorithms by the source of the local discharge signal characteristic vector in the local discharge signal feature samples storehouse Each source categories in classification are combined, and constitute a plurality of sub-classifiers, and with the local discharge signal feature samples Local discharge signal characteristic vector in storehouse builds multi-category support vector machines model as input.

8. the cable local discharge signal recognition method based on S-transformation according to claim 7, it is characterised in that described Using the local discharge signal as sample, the supporting vector machine model is trained, the supporting vector for being trained Machine model includes：

The local discharge signal that separate sources according to the local discharge signal selects the separate sources of equal number at random is made It is training sample, and the training sample is input into the supporting vector machine model to be trained, the support for being trained Vector machine model.

9. the cable local discharge signal recognition method based on S-transformation according to claim 8, it is characterised in that described The characteristic vector of local discharge signal to be identified is input to the supporting vector machine model for training, obtains to be identified The source of local discharge signal includes：

The characteristic vector of local discharge signal to be identified is input into the supporting vector machine model for training to be exported Value, and the output valve is contrasted with the binary number mark of the source categories of the local discharge signal characteristic vector, Obtain the source of local discharge signal to be identified.

10. a kind of cable local discharge signal identification device based on S-transformation, it is characterised in that including：

Conversion module, for obtaining the local discharge signal in known source, and carries out S-transformation to the local discharge signal, obtains To multiple time-frequency matrix；

Decomposing module, obtains modular matrix, and carry out singular value to the modular matrix for carrying out modulus to the multiple time-frequency matrix Decompose, obtain the unusual value sequence of the modular matrix；

Computing module, for the unusual value sequence to be divided into at least two intervals according to the unusual value sequence, calculates every It is individual interval in singular value Shannon entropys and the Shannon entropys of the unusual value sequence ratio, and using the ratio as Local discharge signal characteristic vector sets up local discharge signal feature samples storehouse；

Module is built, for using the local discharge signal characteristic vector in the local discharge signal feature samples storehouse as defeated Enter, build multi-category support vector machines model；

Training module, for using the local discharge signal as sample, being trained to the supporting vector machine model, obtains The supporting vector machine model for training；

Input module, for the characteristic vector of local discharge signal to be identified to be input into the SVMs for training Model, obtains the source of local discharge signal to be identified.