CN107561420A - A kind of cable local discharge signal characteristic vector extracting method based on empirical mode decomposition - Google Patents

A kind of cable local discharge signal characteristic vector extracting method based on empirical mode decomposition Download PDF

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CN107561420A
CN107561420A CN201710762929.1A CN201710762929A CN107561420A CN 107561420 A CN107561420 A CN 107561420A CN 201710762929 A CN201710762929 A CN 201710762929A CN 107561420 A CN107561420 A CN 107561420A
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discharge signal
mrow
local discharge
imf
msub
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吴炬卓
高紫建
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Zhuhai Power Supply Bureau of Guangdong Power Grid Co Ltd
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Zhuhai Power Supply Bureau of Guangdong Power Grid Co Ltd
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Abstract

The present invention provides a kind of cable local discharge signal characteristic vector extracting method based on empirical mode decomposition.A kind of cable local discharge signal characteristic vector extracting method based on empirical mode decomposition, wherein, comprise the following steps：Step 1：Obtain the cable local discharge signal of Site Detection；Step 2：The cable local discharge signal arrived to Site Detection carries out empirical mode decomposition, obtains each intrinsic mode function component；Step 3：The each intrinsic mode function component obtained for step 2, calculates its similar factors with local discharge signal in step 1 respectively；Step 4：The similar factors obtained using step 3, form the characteristic vector of cable local discharge signal.Method proposed by the present invention is primarily based upon the advantage of EMD methods, and the characteristic vector of extraction can preferably characterize local discharge signal.

Description

A kind of cable local discharge signal characteristic vector extraction based on empirical mode decomposition Method
Technical field
The present invention relates to cable local discharge on-line monitoring technique field, and empirical modal is based on more particularly, to one kind The cable local discharge signal characteristic vector extracting method of decomposition.
Background technology
In cable local discharge on-line monitoring, the local discharge signal detected 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 equipment different, criterion Difference, so local discharge signal source is identified important realistic meaning.
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.In feature extracting method, Wavelet analysis method is due to good local time-frequency characteristic, being widely used to the feature extraction of local discharge signal, but its The influence of noise is easily received, and mother wavelet is difficult to select.And empirical mode decomposition (empirical mode Decomposition, EMD) local feature time scale of the method based on signal, non-stationary signal can be decomposed into limited base This modal components (intrinsic modefunction, IMF) sum, is a kind of adaptive signal processing method, is adapted to non- Linear and non-stationary process.
The content of the invention
It is an object of the invention to provide a kind of cable local discharge signal characteristic vector extraction based on empirical mode decomposition Method, advantage of this method based on EMD methods, the characteristic vector of extraction can more preferably characterize local discharge signal.
In order to solve the above technical problems, the technical solution adopted by the present invention is：A kind of cable based on empirical mode decomposition Local discharge signal characteristic vector pickup method, wherein, comprise the following steps：
Step 1：Obtain the cable local discharge signal of Site Detection.
Step 2：The cable local discharge signal arrived to Site Detection carries out empirical mode decomposition, obtains each natural mode of vibration Function component；
Step 3：The each intrinsic mode function component obtained for step 2, calculates itself and shelf depreciation in step 1 respectively The similar factors of signal；
Step 4：The similar factors obtained using step 3, form the characteristic vector of cable local discharge signal.
Further, in the step 2, the cable local discharge signal arrived to Site Detection carries out empirical mode decomposition, The step of obtaining each intrinsic mode function component be：
(1) cable local discharge signal x (t) all local maximums and local minimum are obtained；To these extreme points Cubic spline interpolation is carried out, the coenvelope line that is made up of all Local modulus maximas is obtained and all local minizing points forms Lower envelope line, is designated as e respectivelymaxAnd e (t)min(t)；And calculate average m (t)=(e of upper and lower envelopemax(t)+emin(t))/ 2；
(2) details d (t)=x (t)-m (t) is extracted, if d (t) meets in whole data sequence, the quantity of extreme point Or at most difference 1, and on time shaft Local Symmetric, then d (t) be first natural mode of vibration equal with the quantity of zero crossing Function (IMF) component, it is input repeat step 1 with d (t) otherwise), until obtaining first IMF component, it is designated as IMF1(t)；
(3) remainder r is remembered1(t)=x (t)-IMF1(t), and as new new signal repeat step 1 to be analyzed) to step 2), to obtain second IMF component, it is designated as IMF2(t), now, remainder r2(t)=r1(t)-IMF2(t)；Repeat the above steps, Until obtained remainder rn(t) when being that a monotonic signal or its value are less than some previously given threshold value, decomposition terminates；
(4) finally, n IMF components IMF is obtained1(t), IMF2(t)…IMFnAnd remainder r (t)n(t), then cable is local Discharge signal x (t) can be expressed as：
Further, in the step 3, each intrinsic mode function component for being obtained to step 2 calculates itself and step respectively It is the step of the similar factors of local discharge signal in rapid 1：
(1) each IMF components and local discharge signal are represented as matrix form, use AiEach IMF components are represented, X is represented Local discharge signal；
(2) for each IMF component, its similar factors with local discharge signal is calculated as follows：
In formula, (AiX) representing matrix AiDot product matrix X, (Ai·Ai) representing matrix AiDot product matrix Ai, (XX) is represented Matrix X dot product matrixes X.
Further, in the step 4, the similar factors that are obtained using step 3, the spy of cable local discharge signal is formed Levying vectorial step is：
λ=[λ1,λ2,...λn]
In formula, λ is the characteristic vector of cable local discharge signal.
Compared with prior art, its advantage is the present invention：
Method proposed by the present invention is primarily based upon the advantage of EMD methods, and the characteristic vector of extraction can be characterized preferably Local discharge signal.
Brief description of the drawings
Fig. 1 is the principle process schematic diagram of the present invention.
Embodiment
Accompanying drawing being given for example only property explanation, it is impossible to be interpreted as the limitation to this patent；It is attached in order to more preferably illustrate the present embodiment Scheme some parts to have omission, zoom in or out, do not represent the size of actual product；To those skilled in the art, Some known features and its explanation may be omitted and will be understood by accompanying drawing.Being given for example only property of position relationship described in accompanying drawing Explanation, it is impossible to be interpreted as the limitation to this patent.
As shown in figure 1, a kind of cable local discharge signal characteristic vector extracting method based on empirical mode decomposition, its In, comprise the following steps：
Step 1：Obtain the cable local discharge signal of Site Detection.
Step 2：The cable local discharge signal arrived to Site Detection carries out empirical mode decomposition, obtains each natural mode of vibration Function component.Specifically, comprise the following steps：
(1) cable local discharge signal x (t) all local maximums and local minimum are obtained；To these extreme points Cubic spline interpolation is carried out, the coenvelope line that is made up of all Local modulus maximas is obtained and all local minizing points forms Lower envelope line, is designated as e respectivelymaxAnd e (t)min(t)；And calculate average m (t)=(e of upper and lower envelopemax(t)+emin(t))/ 2；
(2) details d (t)=x (t)-m (t) is extracted, if d (t) meets in whole data sequence, the quantity of extreme point Or at most difference 1, and on time shaft Local Symmetric, then d (t) be first natural mode of vibration equal with the quantity of zero crossing Function (IMF) component, it is input repeat step 1 with d (t) otherwise), until obtaining first IMF component, it is designated as IMF1(t)；
(3) remainder r is remembered1(t)=x (t)-IMF1(t), and as new new signal repeat step 1 to be analyzed) to step 2), to obtain second IMF component, it is designated as IMF2(t), now, remainder r2(t)=r1(t)-IMF2(t)；Repeat the above steps, Until obtained remainder rn(t) when being that a monotonic signal or its value are less than some previously given threshold value, decomposition terminates；
(4) finally, n IMF components IMF is obtained1(t), IMF2(t)…IMFnAnd remainder r (t)n(t), then cable is local Discharge signal x (t) can be expressed as：
Step 3：The each intrinsic mode function component obtained for step 2, calculates itself and shelf depreciation in step 1 respectively The similar factors of signal.Specifically, comprise the following steps：
(1) each IMF components and local discharge signal are represented as matrix form, use AiEach IMF components are represented, X is represented Local discharge signal；
(2) for each IMF component, its similar factors with local discharge signal is calculated as follows：
In formula, (AiX) representing matrix AiDot product matrix X, (Ai·Ai) representing matrix AiDot product matrix Ai, (XX) is represented Matrix X dot product matrixes X.
Step 4：The similar factors obtained using step 3, form the characteristic vector of cable local discharge signal.Specifically, Comprise the following steps：
λ=[λ1,λ2,...λn]
In formula, λ is the characteristic vector of cable local discharge signal.
Obviously, the above embodiment of the present invention is just for the sake of clearly demonstrating example of the present invention, and is not Restriction to embodiments of the present invention.For those of ordinary skill in the field, on the basis of the above description also It can make other changes in different forms.There is no necessity and possibility to exhaust all the enbodiments.It is all All any modification, equivalent and improvement made within the spirit and principles in the present invention etc., should be included in right of the present invention will Within the protection domain asked.

Claims (4)

  1. A kind of 1. cable local discharge signal characteristic vector extracting method based on empirical mode decomposition, it is characterised in that including Following steps：
    Step 1：Obtain the cable local discharge signal of Site Detection；
    Step 2：The cable local discharge signal arrived to Site Detection carries out empirical mode decomposition, obtains each intrinsic mode function Component；
    Step 3：The each intrinsic mode function component obtained for step 2, calculates itself and local discharge signal in step 1 respectively Similar factors；
    Step 4：The similar factors obtained using step 3, form the characteristic vector of cable local discharge signal.
  2. A kind of 2. cable local discharge signal characteristic vector extraction side based on empirical mode decomposition according to claim 1 Method, it is characterised in that in the step 2, the cable local discharge signal arrived to Site Detection carries out empirical mode decomposition, obtains The step of each intrinsic mode function component is：
    (1) cable local discharge signal x (t) all local maximums and local minimum are obtained；These extreme points are carried out Cubic spline interpolation, obtains the coenvelope line that is made up of all Local modulus maximas and lower bag that all local minizing points are formed Winding thread, e is designated as respectivelymaxAnd e (t)min(t)；And calculate average m (t)=(e of upper and lower envelopemax(t)+emin(t))/2；
    (2) details d (t)=x (t)-m (t) is extracted, if d (t) meets in whole data sequence, the quantity and mistake of extreme point The quantity of zero point is equal or at most differs 1, and on time shaft Local Symmetric, then d (t) is first intrinsic mode function (IMF) component, it is input repeat step 1 with d (t) otherwise), until obtaining first IMF component, it is designated as IMF1(t)；
    (3) remainder r is remembered1(t)=x (t)-IMF1(t), and as new new signal repeat step 1 to be analyzed) to step 2), with Second IMF component is obtained, is designated as IMF2(t), now, remainder r2(t)=r1(t)-IMF2(t)；Repeat the above steps, until Obtained remainder rn(t) when being that a monotonic signal or its value are less than some previously given threshold value, decomposition terminates；
    (4) finally, n IMF components IMF is obtained1(t), IMF2(t)…IMFnAnd remainder r (t)n(t), then cable local discharge Signal x (t) can be expressed as：
    <mrow> <mi>x</mi> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>=</mo> <munderover> <mi>&amp;Sigma;</mi> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>n</mi> </munderover> <msub> <mi>IMF</mi> <mi>i</mi> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>+</mo> <msub> <mi>r</mi> <mi>n</mi> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </mrow>
  3. A kind of 3. cable local discharge signal characteristic vector extraction side based on empirical mode decomposition according to claim 1 Method, it is characterised in that in the step 3, each intrinsic mode function component for being obtained for step 2 calculates itself and step respectively It is the step of the similar factors of local discharge signal in rapid 1：
    (1) each IMF components and local discharge signal are represented as matrix form, use AiEach IMF components are represented, X represents local and put Electric signal；
    (2) for each IMF component, its similar factors with local discharge signal is calculated as follows：
    <mrow> <msub> <mi>&amp;lambda;</mi> <mi>i</mi> </msub> <mo>=</mo> <mfrac> <mrow> <mi>s</mi> <mi>u</mi> <mi>m</mi> <mrow> <mo>(</mo> <msub> <mi>A</mi> <mi>i</mi> </msub> <mo>&amp;CenterDot;</mo> <mi>X</mi> <mo>)</mo> </mrow> </mrow> <msqrt> <mrow> <mi>s</mi> <mi>u</mi> <mi>m</mi> <mrow> <mo>(</mo> <msub> <mi>A</mi> <mi>i</mi> </msub> <mo>&amp;CenterDot;</mo> <msub> <mi>A</mi> <mi>i</mi> </msub> <mo>)</mo> </mrow> <mo>&amp;times;</mo> <mi>s</mi> <mi>u</mi> <mi>m</mi> <mrow> <mo>(</mo> <mi>X</mi> <mo>&amp;CenterDot;</mo> <mi>X</mi> <mo>)</mo> </mrow> </mrow> </msqrt> </mfrac> </mrow>
    In formula, (AiX) representing matrix AiDot product matrix X, (Ai·Ai) representing matrix AiDot product matrix Ai, (XX) representing matrix X dot product matrixes X.
  4. A kind of 4. cable local discharge signal characteristic vector extraction side based on empirical mode decomposition according to claim 1 Method, it is characterised in that in the step 4, the similar factors that are obtained using step 3, form the feature of cable local discharge signal Vector step be：
    λ=[λ1,λ2,...λn]
    In formula, λ is the characteristic vector of cable local discharge signal.
CN201710762929.1A 2017-08-30 2017-08-30 A kind of cable local discharge signal characteristic vector extracting method based on empirical mode decomposition Pending CN107561420A (en)

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CN108562837A (en) * 2018-04-19 2018-09-21 江苏方天电力技术有限公司 A kind of power plant's partial discharge of switchgear ultrasonic signal noise-reduction method
CN109387757A (en) * 2018-12-14 2019-02-26 广东电网有限责任公司 A kind of local discharge signal characteristic vector pickup method
CN111239554B (en) * 2019-11-29 2021-04-13 深圳供电局有限公司 Ultrasonic partial discharge detection analysis model based on big data
CN112946369A (en) * 2021-01-25 2021-06-11 西安理工大学 Cable aging identification method using power line communication equipment

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CN105182172A (en) * 2015-07-23 2015-12-23 广东电网有限责任公司电力科学研究院 Vibration signal pattern spectrum-based method for diagnosing winding conditions under sudden short circuit of transformer
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Cited By (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108562837A (en) * 2018-04-19 2018-09-21 江苏方天电力技术有限公司 A kind of power plant's partial discharge of switchgear ultrasonic signal noise-reduction method
CN109387757A (en) * 2018-12-14 2019-02-26 广东电网有限责任公司 A kind of local discharge signal characteristic vector pickup method
CN109387757B (en) * 2018-12-14 2020-12-04 广东电网有限责任公司 Partial discharge signal feature vector extraction method
CN111239554B (en) * 2019-11-29 2021-04-13 深圳供电局有限公司 Ultrasonic partial discharge detection analysis model based on big data
CN112946369A (en) * 2021-01-25 2021-06-11 西安理工大学 Cable aging identification method using power line communication equipment
CN112946369B (en) * 2021-01-25 2024-04-26 西安理工大学 Cable aging identification method using power line communication device

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Application publication date: 20180109