CN104765971B - A kind of crosslinked polyethylene high-tension cable local discharge characteristic extracting method - Google Patents

A kind of crosslinked polyethylene high-tension cable local discharge characteristic extracting method Download PDF

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CN104765971B
CN104765971B CN201510193461.XA CN201510193461A CN104765971B CN 104765971 B CN104765971 B CN 104765971B CN 201510193461 A CN201510193461 A CN 201510193461A CN 104765971 B CN104765971 B CN 104765971B
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CN104765971A (en
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陈继开
李国庆
张喜林
王振浩
庞丹
李扬
王朝斌
刘博文
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State Grid Corp of China SGCC
State Grid Jilin Electric Power Corp
Northeast Electric Power University
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State Grid Corp of China SGCC
Northeast Dianli University
State Grid Jilin Electric Power Corp
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Abstract

A kind of extracting method of crosslinked polyethylene high-tension cable local discharge characteristic, belongs to high-tension cable technical field.Present invention aim to address present in current high-tension cable partial discharge detection method the problem of partial discharge feature extraction inaccuracy, there is provided a kind of method extracted to high-tension cable partial discharge feature.The collection of protective metal shell earth current signal when the present invention realizes that crosslinked polyethylene high-tension cable partial discharge occurs first with HF current transformer, this current signal is converted into data signal again, then make WAVELET PACKET DECOMPOSITION to data signal and modulus maximum extraction and Singularity Detection are carried out successively to the small echo packet node coefficient after decomposition or reconstruction signal and carry out wavelet-packet energy Exponential Entropy computing, discharge characteristic curve is finally drawn according to operation result, realizes the extraction of crosslinked polyethylene high-tension cable local discharge characteristic.The present invention can improve the extraction accuracy of cable partial discharge feature, and the identification for next step high-tension cable partial discharge provides technical support.

Description

A kind of crosslinked polyethylene high-tension cable local discharge characteristic extracting method
Technical field
The invention belongs to high-tension cable technical field.
Background technology
The internal shelf depreciation of crosslinked polyethylene high voltage power cable (lower abbreviation cable) (partial discharge, PD the electric discharge phenomena occurred in cable insulation structure in some region, the insulation knot that this electric discharge can be to the district cable) are referred to It is configured to damage, if shelf depreciation (lower abbreviation partial discharge) long-term existence, may causes cable major insulation electric under certain condition The decline of gas intensity, cable major insulation penetrability is caused to puncture when serious.Online currently for high-tension cable Partial discharge signal is adopted Diversity method is mainly broadband Electromagnetic coupling method, cable cover(ing) pair when being occurred using HF current transformer (HFCT) collection partial discharge Earth pulse electric current.Studies have shown that strong electromagnetic be present because cable tunnel inner cable is large number of, high frequency electric in addition Transformer and the mismatch of apparatus measures interface impedance, the pulsed current signal feature that cable partial discharge is formed often are submerged in background In noise, even if filtering its partial discharge feature still unobvious by software and hardware, if to doubtful Partial discharge signal merely by judgement Whether its discharge parameter is located at prescribed limit to determine whether it belongs to cable partial discharge, it is possible to causes the hair failed to judge and judged by accident It is raw.
The content of the invention
It is inaccurate present invention aim to address partial discharge feature extraction present in current high-tension cable partial discharge detection method The problem of, there is provided a kind of method extracted to high-tension cable partial discharge feature.
The present invention step be：
A, signal is changed：Cable cover(ing) earth current signal is gathered using HFCT sensors and is converted to voltage signal, profit Voltage signal is converted into 16 position digital signals with analog-digital converter；
B, filter：16 position digital signals of acquisition are filtered using digital band-pass filter, frequency range is obtained and exists 0.5~20MHz 16 position digital signals；
C, DB4 WAVELET PACKET DECOMPOSITIONs are carried out to 16 position digital signals, makes the different frequency component of 16 position digital signals correspondingly It is distributed in different wavelet packet yardsticks, then the small echo packet node coefficient or reconstruction signal obtained to WAVELET PACKET DECOMPOSITION carries out partial discharge Feature information extraction；
The process of partial discharge feature information extraction is：
1. modulus maximum extraction and Singularity Detection are carried out to small echo packet node coefficient or reconstruction signal：To each wavelet packet section Dot factor or reconstruction signal ask for the maximum of mould, and the modulus maximum point convergence in each wavelet scale is singular point, utilizes threshold value Method filters out the abnormal small echo packet node coefficient or reconstruction signal of Singularity Degree；
2. nergy Index entropy computing is carried out to the abnormal wavelet packet coefficient of Singularity Degree or reconstruction signal：
Discrete wavelet packet node coefficient or reconstruction signal matrix are D={ dI, j(k), k=1 ..., L, 1≤i≤n, j= 1 ..., 2i, L is measured signal initial data length, and a slip data window is defined on D, and window width is w ∈ N, slippage factor For δ ∈ N, the slip data window is expressed as：
In above formula, m=1,2 ..., M, M=(L-w)/δ, di,j(k) it is k-th of discrete wavelet packet of small echo packet node (i, j) Coefficient or reconstruction signal, k are element position variable in discrete wavelet packet coefficient or reconstruction signal matrix, and n is the Decomposition order upper limit, M is wavelet-packet energy Exponential Entropy length；Then the process of wavelet-packet energy Exponential Entropy computing is：It is signal with (m + w/2) centered on the moment, window width be w ∈ N slide data window in yardstick i upper 2iIndividual wavelet packet coefficient group or reconstruction signal Energy and, whereinFor upper j-th of the section of the interior yardstick i of (m+w/2) moment time slip-window W (m, w, δ) Point wavelet packet coefficient or reconstruction signal energy and；Make pm(j)=Em(j)/E (m) andThen (m+w/2) moment Wavelet-packet energy Exponential Entropy is：
In above formula, e is the truth of a matter of right logarithmic function
D, it is D={ d in discrete wavelet packet node coefficient or reconstruction signal matrixI, j(k), k=1 ..., L, 1≤i≤n, j =1 ..., 2iOn mobile time slip-window W (m, w, δ), repeat step c, until m=M, finally give a wavelet-packet energy Exponential Entropy arrayUsing the time as abscissa, wavelet-packet energy refers to Number entropy is ordinate, draws cable partial discharge indicatrix.
Slip data window width w and slippage factor δ of the present invention specific establishing method is：To typical cable partial discharge Pulse width is counted, and makes wmaxCable partial discharge pulse width maximum is represented, makes wminRepresent partial discharge characteristic pulse width most Small value, then the alternative condition for sliding data window width w is wmin≤w≤wmax；Slippage factor δ alternative conditions be 1≤δ≤ 0.2wmin。
The present invention proposes a kind of high-tension cable partial discharge feature extracting method based on wavelet-packet energy index entropy theory, utilizes The method can improve the extraction accuracy of cable partial discharge feature, and the identification for next step high-tension cable partial discharge provides technical support. The positive effect of the present invention：
(1) wavelet-packet energy Exponential Entropy proposed by the present invention possesses multiple dimensioned differentiate of wavelet packet and analyzed and Exponential Entropy statistics Dual characteristicses, it is that Wavelet Analysis Theory and entropy statistical theory are organically blended, small echo packet node coefficient or reconstruction signal is entered The computing of row nergy Index entropy can further portray the temporal variations of sheath earth current signal frequency when cable partial discharge occurs, and carry The high extraction accuracy to cable partial discharge feature.
(2) wavelet-packet energy Exponential Entropy proposed by the present invention is to carry out energy to small echo packet node coefficient or reconstruction signal to refer to Number entropy computing, so as to the problem of effectively having evaded the undefined value and null value defined with logarithm in comentropy, overcomes Shannon entropy Deficiency.
Brief description of the drawings
Fig. 1 is using the method for the present invention, and what is collected when sample frequency be 100MHz includes cable partial discharge feature Raw voltage signals oscillogram；
Fig. 2 is the partial discharge signature waveform figure extracted using the inventive method from Fig. 1 raw voltage signals；
Fig. 3 is the raw voltage signals waveform not comprising cable partial discharge feature collected when sample frequency is 100MHz Figure；
Fig. 4 is to carry out the oscillogram that wavelet-packet energy Exponential Entropy computing obtains to Fig. 3 primary signals using the inventive method.
Embodiment
The present invention step be：
A, signal is changed：Cable cover(ing) earth current signal is gathered using HFCT sensors and is converted to voltage signal, profit Voltage signal is converted into 16 position digital signals with analog-digital converter；
B, filter：16 position digital signals of acquisition are filtered using digital band-pass filter, frequency range is obtained and exists 0.5~20MHz 16 position digital signals；
C, to 16 position digital signal progress DB4 WAVELET PACKET DECOMPOSITIONs, (WAVELET PACKET DECOMPOSITION number of plies selection range is shown in that right will 2) different frequency component that asking in book, makes 16 position digital signals is correspondingly distributed in different wavelet packet yardsticks (for example, through 4 Layer WAVELET PACKET DECOMPOSITION, primary signal are assigned to corresponding 16 frequency bands：(0~1.25MHz), (1.25~2.50MHz), (2.50 ~3.75MHz), (3.75~5.00MHz), (5.00~6.25MHz), (6.25~7.50MHz), (7.50~8.75MHz), (8.75~10.00MHz), (10.00~11.25MHz), (11.25~12.50MHz), (12.50~13.75MHz), (13.75 ~15.00MHz), (15.00~16.25MHz), (16.25~17.50MHz), (17.50~18.75MHz), (18.75~ 20.00MHz)), then to WAVELET PACKET DECOMPOSITION the small echo packet node coefficient or reconstruction signal obtained carries out partial discharge feature information extraction；
The process of partial discharge feature information extraction is：
1. modulus maximum extraction and Singularity Detection are carried out to small echo packet node coefficient or reconstruction signal：To each wavelet packet section Dot factor or reconstruction signal ask for the maximum of mould, and the modulus maximum point convergence in each wavelet scale is singular point, utilizes threshold value Method filters out the abnormal small echo packet node coefficient or reconstruction signal of Singularity Degree；
2. nergy Index entropy computing is carried out to the abnormal wavelet packet coefficient of Singularity Degree or reconstruction signal：
Discrete wavelet packet node coefficient or reconstruction signal matrix are D={ dI, j(k), k=1 ..., L, 1≤i≤n, j= 1 ..., 2i, L is measured signal initial data length, and a slip data window is defined on D, and window width is w ∈ N, slippage factor For δ ∈ N, the slip data window is expressed as：
In above formula, m=1,2 ..., M, M=(L-w)/δ, di,j(k) it is k-th of discrete wavelet packet of small echo packet node (i, j) Coefficient or reconstruction signal, k are element position variable in discrete wavelet packet coefficient or reconstruction signal matrix, and n is the Decomposition order upper limit, M is wavelet-packet energy Exponential Entropy length；Then the process of wavelet-packet energy Exponential Entropy computing is：It is signal with (m+ W/2) the yardstick i upper 2 centered on the moment, in the slip data window that window width is w ∈ NiIndividual wavelet packet coefficient group or reconstruction signal Energy and, whereinFor upper j-th of the section of the interior yardstick i of (m+w/2) moment time slip-window W (m, w, δ) Point wavelet packet coefficient or reconstruction signal energy and；Make pm(j)=Em(j)/E (m) and, then (m+w/2) moment Wavelet-packet energy Exponential Entropy is：
In above formula, e is the truth of a matter of right logarithmic function
D, it is D={ d in discrete wavelet packet node coefficient or reconstruction signal matrixI, j(k), k=1 ..., L, 1≤i≤n, j =1 ..., 2iOn mobile time slip-window W (m, w, δ), repeat step c, until m=M, finally give a wavelet-packet energy Exponential Entropy arrayUsing the time as horizontal seat Mark, wavelet-packet energy Exponential Entropy is ordinate, draws cable partial discharge indicatrix.
Slip data window width w and slippage factor δ of the present invention specific establishing method is：To typical cable partial discharge Pulse width is counted, and makes wmaxCable partial discharge pulse width maximum is represented, makes wminRepresent partial discharge characteristic pulse width most Small value, then the alternative condition for sliding data window width w is wmin≤w≤wmax；Slippage factor δ alternative conditions be 1≤δ≤ 0.2wmin。
Using partial discharge feature extracting method provided by the invention to including the raw voltage signals of cable partial discharge feature in Fig. 1 Cable partial discharge feature extraction is carried out, the cable partial discharge feature extracted is as shown in Fig. 2 as shown in Figure 2：Wavelet-packet energy Exponential Entropy There is the pulse (specific waveform is shown in signified ' M ' the type ripple of arrow in Fig. 2) that amplitude is 0.0065pu in moment 11.5ms, and when Carve 11.5ms and cable partial discharge event occurs really, so proving that partial discharge feature extracting method provided by the invention is not only able to extract Partial discharge signature waveform and can indicate that its occur the moment.
The primary voltage for not including cable partial discharge feature in Fig. 3 is believed using partial discharge feature extracting method provided by the invention Number carry out cable partial discharge feature extraction, obtain operation result as shown in figure 4, as shown in Figure 4：Due in raw voltage signals not Comprising cable partial discharge feature, so there is not obvious impulse waveform, wavelet-packet energy index in wavelet packet nergy Index entropy in Fig. 4 Entropy numerical value is substantially constant=0.4169pu, it was demonstrated that cable partial discharge feature is not present in raw voltage signals in Fig. 3, and this period is not sent out Raw cable partial discharge event.

Claims (1)

  1. A kind of 1. crosslinked polyethylene high-tension cable local discharge characteristic extracting method, it is characterised in that：
    A, signal is changed：Cable cover(ing) earth current signal is gathered using HFCT sensors and is converted to voltage signal, utilizes mould Voltage signal is converted to 16 position digital signals by number converter；
    B, filter：16 position digital signals of acquisition are filtered using digital band-pass filter, obtain frequency range 0.5~ 20MHz 16 position digital signals；
    C, DB4 WAVELET PACKET DECOMPOSITIONs are carried out to 16 position digital signals, the different frequency component of 16 position digital signals is correspondingly distributed In different wavelet packet yardsticks, then the small echo packet node coefficient or reconstruction signal obtained to WAVELET PACKET DECOMPOSITION carries out partial discharge feature Information extraction；
    The process of partial discharge feature information extraction is：
    1. modulus maximum extraction and Singularity Detection are carried out to small echo packet node coefficient or reconstruction signal：To each small echo packet node system Number or reconstruction signal ask for the maximum of mould, and the modulus maximum point convergence in each wavelet scale is singular point, is sieved using threshold method Select Singularity Degree abnormal small echo packet node coefficient or reconstruction signal；
    2. nergy Index entropy computing is carried out to the abnormal wavelet packet coefficient of Singularity Degree or reconstruction signal：
    Discrete wavelet packet node coefficient or reconstruction signal matrix are
    D={ dI, j(k), k=1 ..., L, 1≤i≤n, j=1 ..., 2i,
    L is measured signal initial data length, a slip data window is defined on D, window width is w ∈ N, and slippage factor is δ ∈ N, the slip data window are expressed as：
    <mrow> <mi>W</mi> <mrow> <mo>(</mo> <mi>m</mi> <mo>;</mo> <mi>w</mi> <mo>,</mo> <mi>&amp;delta;</mi> <mo>)</mo> </mrow> <mo>=</mo> <mfenced open = "(" close = ")"> <mtable> <mtr> <mtd> <mrow> <msub> <mi>d</mi> <mrow> <mi>i</mi> <mo>,</mo> <mn>1</mn> </mrow> </msub> <mrow> <mo>(</mo> <mn>1</mn> <mo>+</mo> <mi>m</mi> <mi>&amp;delta;</mi> <mo>)</mo> </mrow> </mrow> </mtd> <mtd> <mrow> <msub> <mi>d</mi> <mrow> <mi>i</mi> <mo>,</mo> <mn>1</mn> </mrow> </msub> <mrow> <mo>(</mo> <mn>2</mn> <mo>+</mo> <mi>m</mi> <mi>&amp;delta;</mi> <mo>)</mo> </mrow> </mrow> </mtd> <mtd> <mo>...</mo> </mtd> <mtd> <mrow> <msub> <mi>d</mi> <mrow> <mi>i</mi> <mo>,</mo> <mn>1</mn> </mrow> </msub> <mrow> <mo>(</mo> <mi>w</mi> <mo>+</mo> <mi>m</mi> <mi>&amp;delta;</mi> <mo>)</mo> </mrow> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <msub> <mi>d</mi> <mrow> <mi>i</mi> <mo>,</mo> <mn>2</mn> </mrow> </msub> <mrow> <mo>(</mo> <mn>1</mn> <mo>+</mo> <mi>m</mi> <mi>&amp;delta;</mi> <mo>)</mo> </mrow> </mrow> </mtd> <mtd> <mrow> <msub> <mi>d</mi> <mrow> <mi>i</mi> <mo>,</mo> <mn>2</mn> </mrow> </msub> <mrow> <mo>(</mo> <mn>2</mn> <mo>+</mo> <mi>m</mi> <mi>&amp;delta;</mi> <mo>)</mo> </mrow> </mrow> </mtd> <mtd> <mo>...</mo> </mtd> <mtd> <mrow> <msub> <mi>d</mi> <mrow> <mi>i</mi> <mo>,</mo> <mn>2</mn> </mrow> </msub> <mrow> <mo>(</mo> <mi>w</mi> <mo>+</mo> <mi>m</mi> <mi>&amp;delta;</mi> <mo>)</mo> </mrow> </mrow> </mtd> </mtr> <mtr> <mtd> <mtable> <mtr> <mtd> <mo>.</mo> </mtd> </mtr> <mtr> <mtd> <mo>.</mo> </mtd> </mtr> <mtr> <mtd> <mo>.</mo> </mtd> </mtr> </mtable> </mtd> <mtd> <mtable> <mtr> <mtd> <mo>.</mo> </mtd> </mtr> <mtr> <mtd> <mo>.</mo> </mtd> </mtr> <mtr> <mtd> <mo>.</mo> </mtd> </mtr> </mtable> </mtd> <mtd> <mtable> <mtr> <mtd> <mo>.</mo> </mtd> </mtr> <mtr> <mtd> <mo>.</mo> </mtd> </mtr> <mtr> <mtd> <mo>.</mo> </mtd> </mtr> </mtable> </mtd> <mtd> <mtable> <mtr> <mtd> <mo>.</mo> </mtd> </mtr> <mtr> <mtd> <mo>.</mo> </mtd> </mtr> <mtr> <mtd> <mo>.</mo> </mtd> </mtr> </mtable> </mtd> </mtr> <mtr> <mtd> <mrow> <msub> <mi>d</mi> <mrow> <mi>i</mi> <mo>,</mo> <msup> <mn>2</mn> <mi>i</mi> </msup> </mrow> </msub> <mrow> <mo>(</mo> <mn>1</mn> <mo>+</mo> <mi>m</mi> <mi>&amp;delta;</mi> <mo>)</mo> </mrow> </mrow> </mtd> <mtd> <mrow> <msub> <mi>d</mi> <mrow> <mi>i</mi> <mo>,</mo> <msup> <mn>2</mn> <mi>i</mi> </msup> </mrow> </msub> <mrow> <mo>(</mo> <mn>2</mn> <mo>+</mo> <mi>m</mi> <mi>&amp;delta;</mi> <mo>)</mo> </mrow> </mrow> </mtd> <mtd> <mo>...</mo> </mtd> <mtd> <mrow> <msub> <mi>d</mi> <mrow> <mi>i</mi> <mo>,</mo> <msup> <mn>2</mn> <mi>i</mi> </msup> </mrow> </msub> <mrow> <mo>(</mo> <mi>w</mi> <mo>+</mo> <mi>m</mi> <mi>&amp;delta;</mi> <mo>)</mo> </mrow> </mrow> </mtd> </mtr> </mtable> </mfenced> </mrow>
    In above formula, m=1,2 ..., M,
    M=(L-w)/δ, di,j(k) it is k-th of discrete wavelet packet coefficient of small echo packet node (i, j) or reconstruction signal, k is discrete small Element position variable in ripple bag coefficient or reconstruction signal matrix, n are the Decomposition order upper limit, and M is wavelet-packet energy Exponential Entropy length； Then the process of wavelet-packet energy Exponential Entropy computing is：
    Be signal by (m+w/2) centered on the moment, the yardstick i upper 2 slided in data window that window width is w ∈ Ni The energy of individual wavelet packet coefficient group or reconstruction signal and, whereinFor (m+w/2) moment sliding time The energy of upper j-th of node wavelet packet coefficients of the interior yardstick i of window W (m, w, δ) or reconstruction signal and；Make pm(j)=Em(j)/E(m) andThen the wavelet-packet energy Exponential Entropy at (m+w/2) moment is：
    <mrow> <msubsup> <mi>H</mi> <mrow> <mi>E</mi> <mi>P</mi> <mi>E</mi> </mrow> <mi>i</mi> </msubsup> <mrow> <mo>(</mo> <mi>m</mi> <mo>)</mo> </mrow> <mo>=</mo> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>j</mi> <mo>=</mo> <mn>1</mn> </mrow> <msup> <mn>2</mn> <mi>i</mi> </msup> </munderover> <msub> <mi>p</mi> <mi>m</mi> </msub> <mo>(</mo> <mi>j</mi> <mo>)</mo> <msup> <mi>e</mi> <mrow> <mo>(</mo> <mrow> <mn>1</mn> <mo>-</mo> <msub> <mi>p</mi> <mi>m</mi> </msub> <mrow> <mo>(</mo> <mi>j</mi> <mo>)</mo> </mrow> </mrow> <mo>)</mo> </mrow> </msup> </mrow>
    In above formula, e is the truth of a matter of right logarithmic function
    D, it is in discrete wavelet packet node coefficient or reconstruction signal matrix
    D={ dI, j(k), k=1 ..., L, 1≤i≤n, j=1 ..., 2iOn mobile time slip-window W (m, w, δ), repeat step C, until m=M, finally
    Obtain a wavelet-packet energy Exponential Entropy array
    <mrow> <mi>H</mi> <mo>=</mo> <mo>{</mo> <msubsup> <mi>H</mi> <mrow> <mi>P</mi> <mi>E</mi> <mi>E</mi> </mrow> <mi>i</mi> </msubsup> <mrow> <mo>(</mo> <mi>m</mi> <mo>)</mo> </mrow> <mo>,</mo> <mi>m</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mo>...</mo> <mo>,</mo> <mi>M</mi> <mo>,</mo> <mn>1</mn> <mo>&amp;le;</mo> <mi>i</mi> <mo>&amp;le;</mo> <mi>n</mi> <mo>,</mo> <mi>M</mi> <mo>=</mo> <mrow> <mo>(</mo> <mi>L</mi> <mo>-</mo> <mi>w</mi> <mo>)</mo> </mrow> <mo>/</mo> <mi>&amp;delta;</mi> <mo>}</mo> </mrow> ,
    Using the time as abscissa, wavelet-packet energy Exponential Entropy is ordinate, draws cable partial discharge indicatrix；
    Described slip data window width w and slippage factor δ specific establishing method is：Typical cable partial discharge pulse width is entered Row statistics, makes wmaxCable partial discharge pulse width maximum is represented, makes wminPartial discharge characteristic pulse width minimum is represented, then is slided Data window width w alternative condition is wmin≤w≤wmax；Slippage factor δ alternative conditions are 1≤δ≤02wmin。
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