CN113640566B - FOCT drift fault feature extraction method - Google Patents
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Abstract
The invention discloses a FOCT drift fault feature extraction method, which comprises the steps of obtaining an output signal x (t) of an optical fiber current transformer containing a drift error signal; decomposing the output signal x (t) into a plurality of intrinsic scale components containing error information and a residual signal containing error information using an improved local feature scale decomposition algorithm; respectively calculating sample entropy of each intrinsic scale component and the residual signal to form an error signal component data set; superposing error signal components in the error signal component data set to obtain an error signal; and selecting a peak-tip structure with monotonically rising amplitude in a certain time in the error signal as the characteristic of drift fault according to the time domain image of the error signal. The method is suitable for extracting the fault characteristics of the optical fiber current transformer, improves the accuracy and the instantaneity of the characteristic extraction, and further improves the fault research and judgment speed.
Description
Technical Field
The invention relates to a FOCT drift fault feature extraction method, and belongs to the technical field of fault detection of optical fiber current transformers.
Background
The fiber current transformer (FOCT) based on Faraday effect has the advantages of good insulation performance, high reliability, wide band frequency domain and the like, and is widely applied to high-voltage direct current transmission engineering. However, in the long-term operation process of the optical fiber current transformer, the optical fiber current transformer is influenced by the complex environment in the transformer substation, and the performance of the optical fiber current transformer is deteriorated and even causes operation accidents.
The faults of the optical fiber current transformer are mainly represented by complete failure faults, fixed deviation faults, drift deviation faults, precision reduction faults and the like. The drift deviation fault is the most common FOCT fault, and the fault cause can be analyzed by collecting fault signals and extracting fault characteristics, so that a basis is provided for realizing rapid diagnosis and positioning of faults.
Signal feature extraction methods for optical fiber current transformers are generally classified into three categories: time domain signal analysis, frequency domain signal analysis, and time frequency signal analysis. The mature time-frequency signal analysis method mainly comprises Hilbert transformation, wavelet transformation, empirical mode decomposition and the like. However, the methods have the phenomena of envelope lack, endpoint response, modal aliasing and the like, so that the decomposition result has errors, the operation process is too complex, the instantaneity of the extraction result is poor, and the subsequent fault discrimination speed is influenced.
Disclosure of Invention
The purpose is as follows: in order to solve the problems of envelope lack, endpoint response, complex operation and the like in the prior art, the invention provides a FOCT drift fault feature extraction method.
The technical scheme is as follows: in order to solve the technical problems, the invention adopts the following technical scheme:
The invention discloses a FOCT drift fault feature extraction method, which comprises the following steps:
Step 1: an output signal x (t) of the optical fiber current transformer containing the drift error signal is obtained.
Step 2: the output signal x (t) is decomposed using a modified local feature scale decomposition algorithm into a plurality of intrinsic scale components containing error information and a residual signal containing error information.
Step 3: sample entropy of each intrinsic scale component and residual signal is calculated respectively to obtain intrinsic scale component sample entropy and residual signal sample entropy, the intrinsic scale component sample entropy larger than the threshold value is used as an error signal component, the residual signal sample entropy larger than the threshold value is used as an error signal component, and the error signal components are combined to form an error signal component data set.
Step 4: and superposing error signal components in the error signal component data set to obtain an error signal.
Step 5: and selecting a peak-tip structure with monotonically rising amplitude in a certain time in the error signal as the characteristic of drift fault according to the time domain image of the error signal.
Preferably, the calculation formula of the output signal x (t) of the optical fiber current transformer containing the drift error signal is as follows:
x(t)＝0.5K[1+cos(Δθ+ωt)]+K1t'
Wherein: k represents a photoelectric loop parameter, k=k pLI0,Kp represents a photoelectric conversion coefficient of the photoelectric detector, L represents optical path loss, I 0 represents light intensity transmitted by the light source, Δθ represents faraday effect, Δθ=4vni, v represents Verdet constant of the optical fiber, N represents sensing loop number of the optical fiber, I represents current to be measured, ω represents signal frequency, t represents drift deviation coefficient, and t' represents deviation occurrence time.
Preferably, the improved local feature scale decomposition algorithm comprises the following steps:
(2-1) calculating all extreme points X k of the output signal X (t) and corresponding moments τ k, k=1, 2.
(2-2) Calculating a straight line L k, k=1, 2, formed by any two adjacent maximum or minimum extreme points X k and X k+2, finding a time τ k+1 corresponding to an extreme point X k+1 between all adjacent maximum or minimum extreme points X k and X k+2, and calculating a function value at τ k+1, denoted as a k+1, and a corresponding value of L k+1:
Lk+1＝αAk+1+(1-α)Xk+1,k＝1,2,...M-2
Wherein: A k+1 represents the function value at time τ k+1 corresponding to the extreme point X k+1 between the extreme points X k and X k+2; l k+1 represents the average of A k+1 and X k+1, and the time τ k+2 corresponding to the maximum or minimum extreme point X k+2, α represents the scale factor, and typically, α is 0.5.
And (2-3) finally obtaining subscripts of A k and L k as 2..M, carrying out mirror image continuation estimation on L 1 and M 1 at the end points to obtain extreme points (tau 0,X0),(τM+1,XM+1) at the left end and the right end, and respectively obtaining A 1,AM and L 1,LM.
(2-4) Fitting all resulting baseline curves BL 1 (t) using piecewise three Hermite interpolation L 2,L3,...LM-1, fitting L 1-L2,LM-1-LM,BL1 (t) separately using piecewise linear transformation to represent baseline signals
(2-5) Separating the baseline signal BL 1 (t) from the output signal x (t), obtaining a new signal h 1 (t) as
h1(t)＝x(t)-BL1(t)
(2-6) Judging whether h 1 (t) satisfies the intrinsic scale component ISC component discrimination condition:
Within the whole data section of the output signal, strict monotonicity exists between any two adjacent maxima and minima; the ratio of the function value corresponding to the extreme point between any two maximum (small) value points in the whole data segment to the corresponding extreme value is kept unchanged.
If so, outputting isc1=h 1 (t) as a first ISC component; repeating steps (2-1) - (2-6) k times until h k (t) satisfies the ISC component condition, i.e., ISCk =h k (t).
(2-7) Separating ISC1 from the output signal to obtain a new signal r 1 (t):
r1(t)＝x(t)-ISC1
(2-8) repeating steps (2-1) - (2-7) k times with r 1 (t) as the residual component until r k (t) is a constant or monotonic function, obtaining k residual components, and summing the k residual components to obtain a residual component r (t).
(2-9) Will ultimately decompose the output signal x (t) into k ISC j (t) components and one residual component r (t), i.e
As a preferred scheme, the method for calculating sample entropy by the intrinsic scale component and the residual signal comprises the following steps:
(3-1) decomposing the intrinsic scale component and the residual signal into a vector sequence X (i) with a one-dimensional array of m
X(i)＝[y1,y2,...,yi...,ym],i＝1,2,...,m
Y i is the ith one-dimensional array of the decomposition.
(3-2) Defining a distance d (i, j) between vectors y i and y j, where j=1, 2,..m (i+.j)
d(i,j)＝|yi-yj|
(3-3) Setting a threshold r, counting the number b of distances d (i, j) smaller than r, and then comparing the number b with the total number m of distances, and recording asI.e.
Wherein: i=1, 2,..m.
(3-4) B m (r) isAverage value of (2)
(3-5) Increasing the dimension to m+1, repeating the steps (3-1) to (3-4) to obtain B m+1 (r)
(3-6) Sample entropy formula of intrinsic scale component, residual signal as follows:
The beneficial effects are that: the FOCT drift fault feature extraction method provided by the invention overcomes the problems of envelope underenvelope, endpoint response, complex operation and the like of the existing fault feature extraction algorithm, and improves fitting precision by using three Hermite interpolation; the end effect is reduced by using piecewise linear variation for the left and right end points respectively. And decomposing the output signal of the optical fiber current transformer into infinite harmonic components by using a Bessel decomposition algorithm, and solving the primary current by using the ratio of fundamental waves to second harmonics. And extracting fault components in the current by using a local feature scale decomposition algorithm, calculating sample entropy of each component, judging error features according to the value of the sample entropy, and obtaining fault feature vectors. The method is suitable for extracting the fault characteristics of the optical fiber current transformer, improves the accuracy and the instantaneity of the characteristic extraction, and further improves the fault research and judgment speed.
Drawings
Fig. 1 is a flow chart of the disclosed method.
FIG. 2 is a flow chart of a modified local feature scale decomposition algorithm in the method of the present disclosure.
FIG. 3 is a graph showing the result of error signal recombination under drift bias.
Detailed Description
The invention will be further described with reference to specific examples.
As shown in fig. 1, the invention discloses a FOCT drift fault feature extraction method, which comprises the following steps:
Step 1: an output signal x (t) of the optical fiber current transformer containing the drift error signal is obtained.
Step 2: an improved local feature scale decomposition algorithm (LCD) is used to decompose the output signal x (t) into a plurality of intrinsic scale components containing error information and a residual signal containing error information.
Step 3: sample entropy of each intrinsic scale component and residual signal is calculated respectively to obtain intrinsic scale component sample entropy and residual signal sample entropy, the intrinsic scale component sample entropy larger than the threshold value is used as an error signal component, the residual signal sample entropy larger than the threshold value is used as an error signal component, and the error signal components are combined to form an error signal component data set.
Step 4: and superposing error signal components in the error signal component data set to obtain an error signal.
Step 5: and selecting a peak-tip structure with monotonically rising amplitude in a certain time in the error signal as the characteristic of drift fault according to the time domain image of the error signal.
As shown in fig. 2, the output signal x (t) of the optical fiber current transformer containing the drift error signal has the following calculation formula:
x(t)＝0.5K[1+cos(Δθ+ωt)]+K1t'
Wherein: k represents a photoelectric loop parameter, k=k pLI0,Kp represents a photoelectric conversion coefficient of the photoelectric detector, L represents optical path loss, I 0 represents light intensity transmitted by the light source, Δθ represents faraday effect, Δθ=4vni, v represents Verdet constant of the optical fiber, N represents sensing loop number of the optical fiber, I represents current to be measured, ω represents signal frequency, t represents drift deviation coefficient, and t' represents deviation occurrence time.
The improved local feature scale decomposition algorithm comprises the following steps:
(2-1) calculating all extreme points X k of the output signal X (t) and corresponding moments τ k, k=1, 2.
(2-2) Calculating a straight line L k, k=1, 2, formed by any two adjacent maximum or minimum extreme points X k and X k+2, finding a time τ k+1 corresponding to an extreme point X k+1 between all adjacent maximum or minimum extreme points X k and X k+2, and calculating a function value at τ k+1, denoted as a k+1, and a corresponding value of L k+1:
Lk+1＝αAk+1+(1-α)Xk+1,k＝1,2,...M-2
Wherein: A k+1 represents the function value at time τ k+1 corresponding to the extreme point X k+1 between the extreme points X k and X k+2; l k+1 represents the average of A k+1 and X k+1, and the time τ k+2 corresponding to the maximum or minimum extreme point X k+2, α represents the scale factor, and typically, α is 0.5.
And (2-3) finally obtaining subscripts of A k and L k as 2..M, carrying out mirror image continuation estimation on L 1 and M 1 at the end points to obtain extreme points (tau 0,X0),(τM+1,XM+1) at the left end and the right end, and respectively obtaining A 1,AM and L 1,LM.
(2-4) Fitting all resulting baseline curves BL 1 (t) using piecewise three Hermite interpolation L 2,L3,...LM-1, fitting L 1-L2,LM-1-LM,BL1 (t) separately using piecewise linear transformation to represent baseline signals
(2-5) Separating the baseline signal BL 1 (t) from the output signal x (t), obtaining a new signal h 1 (t) as
h1(t)＝x(t)-BL1(t)
(2-6) Judging whether h 1 (t) satisfies the intrinsic scale component ISC component discrimination condition:
Within the whole data section of the output signal, strict monotonicity exists between any two adjacent maxima and minima; the ratio of the function value corresponding to the extreme point between any two maximum (small) value points in the whole data segment to the corresponding extreme value is kept unchanged.
If so, outputting isc1=h 1 (t) as a first ISC component; repeating steps (2-1) - (2-6) k times until h k (t) satisfies the ISC component condition, i.e., ISCk =h k (t).
(2-7) Separating ISC1 from the output signal to obtain a new signal r 1 (t):
r1(t)＝x(t)-ISC1
(2-8) repeating steps (2-1) - (2-7) k times with r 1 (t) as the residual component until r k (t) is a constant or monotonic function, obtaining k residual components, and summing the k residual components to obtain a residual signal r (t).
(2-9) Will ultimately decompose the output signal x (t) into k ISC j (t) components and one residual signal r (t), i.e
The method for calculating sample entropy by the intrinsic scale component and the residual signal comprises the following steps:
(3-1) decomposing the intrinsic scale component and the residual signal into a vector sequence X (i) with a one-dimensional array of m
X(i)＝[y1,y2,...,yi...,ym],i＝1,2,...,m
Y i is the ith one-dimensional array of the decomposition.
(3-2) Defining a distance d (i, j) between vectors y i and y j, where j=1, 2,..m (i+.j)
d(i,j)＝|yi-yj|
(3-3) Setting a threshold r, counting the number b of distances d (i, j) smaller than r, and then comparing the number b with the total number m of distances, and recording asI.e.
Wherein: i=1, 2,..m.
(3-4) B m (r) isAverage value of (2)
(3-5) Increasing the dimension to m+1, repeating the steps (3-1) to (3-4) to obtain B m+1 (r)
(3-6) Sample entropy formula of intrinsic scale component, residual signal as follows:
And taking the intrinsic scale component and the sample entropy which is larger than the threshold value 1 in the sample entropy of the residual signal as error signal components.
As shown in fig. 3, the waveform of the error signal in the time domain image of the error signal starts to monotonically increase in amplitude of the error signal at a time point less than 0.3s, the increasing amplitude is greater than the average amplitude, the time lasts for about 0.4s, the peak is reached, then the waveform starts to decrease, a sharp corner structure appears, and the occurrence of a drift fault is indicated by FOCT.
The foregoing is only a preferred embodiment of the invention, it being noted that: it will be apparent to those skilled in the art that various modifications and adaptations can be made without departing from the principles of the present invention, and such modifications and adaptations are intended to be comprehended within the scope of the invention.
Claims (7)
1. A FOCT drift fault feature extraction method is characterized in that: the method comprises the following steps:
step 1: obtaining an output signal x (t) of the optical fiber current transformer containing a drift error signal;
Step 2: decomposing the output signal x (t) into a plurality of intrinsic scale components containing error information and a residual signal containing error information using an improved local feature scale decomposition algorithm;
Step 3: sample entropy of each intrinsic scale component and residual signal is calculated respectively to obtain intrinsic scale component sample entropy and residual signal sample entropy, the intrinsic scale component sample entropy larger than a threshold value is used as an error signal component, the residual signal sample entropy larger than the threshold value is used as an error signal component, and the error signal components are combined to form an error signal component data set;
Step 4: superposing error signal components in the error signal component data set to obtain an error signal;
Step 5: selecting a peak-tip structure with monotonically rising amplitude in a certain time in the error signal as the characteristic of drift fault according to the time domain image of the error signal;
the improved local feature scale decomposition algorithm comprises the following steps:
(2-1) calculating all extreme points X k of the output signal X (t) and corresponding moments τ k, k=1, 2,..m, M being the number of extreme points;
(2-2) calculating a straight line L k, k=1, 2, formed by any two adjacent maximum or minimum extreme points X k and X k+2, finding a time τ k+1 corresponding to an extreme point X k+1 between all adjacent maximum or minimum extreme points X k and X k+2, and calculating a function value at τ k+1, denoted as a k+1, and a corresponding value of L k+1:
Lk+1＝αAk+1+(1-α)Xk+1,k＝1,2,...M-2
Wherein: A k+1 represents the function value at time τ k+1 corresponding to the extreme point X k+1 between the extreme points X k and X k+2; l k+1 represents the average value of A k+1 and X k+1, the moment tau k+2 corresponding to the maximum or minimum extreme point X k+2, and alpha represents the scale factor;
the subscripts of A k and L k which are finally obtained in (2-3) are 2..M, mirror image continuation estimation is needed to be carried out on L 1 and M 1 at the end points, extreme points (tau 0,X0),(τM+1,XM+1) at the left end and the right end are obtained, and A 1,AM and L 1,LM are respectively obtained;
(2-4) fitting all resulting baseline curves BL 1 (t) using piecewise three Hermite interpolation L 2,L3,...LM-1, fitting L 1-L2,LM-1-LM separately using piecewise linear transformation;
(2-5) separating the baseline signal BL 1 (t) from the output signal x (t), obtaining a new signal h 1 (t) as
h1(t)＝x(t)-BL1(t)
(2-6) Judging whether h 1 (t) satisfies the intrinsic scale component ISC component discrimination condition:
Within the whole data section of the output signal, strict monotonicity exists between any two adjacent maxima and minima; the ratio of the function value corresponding to the extreme value point between any two maximum (small) value points in the whole data segment to the corresponding extreme value is kept unchanged;
if the discrimination condition is satisfied, isc1=h 1 (t) is output as the first ISC component; repeating steps (2-1) - (2-6) k times until h k (t) meets the ISC component condition, i.e., ISCk =h k (t);
(2-7) separating ISC1 from the output signal to obtain a new signal r 1 (t):
r1(t)＝x(t)-ISC1
(2-8) repeating steps (2-1) - (2-7) k times with r 1 (t) as the residual component until r k (t) is a constant or monotonic function to obtain k residual components, and summing the k residual components to obtain a residual signal r (t);
(2-9) will ultimately decompose the output signal x (t) into k ISC j (t) components and one residual signal r (t), i.e
2. The FOCT drift fault signature extraction method as set forth in claim 1, wherein: the optical fiber current transformer containing the drift error signal outputs a signal x (t), and the calculation formula is as follows:
x(t)＝0.5K[1+cos(Δθ+ωt)]+K1t'
Wherein: k represents the photoelectric loop parameter, delta theta is Faraday effect, omega represents signal frequency, t is time, K 1 represents drift deviation coefficient, and t' represents deviation occurrence time.
3. The FOCT drift fault signature extraction method as set forth in claim 2, wherein: k=k pLI0,Kp is a photoelectric conversion coefficient of the photoelectric detector, L is optical path loss, and I 0 is light intensity transmitted by the light source.
4. The FOCT drift fault signature extraction method as set forth in claim 2, wherein: the delta theta=4VNI, V is Verdet constant of the optical fiber, N is the number of sensing loops of the optical fiber, and I is the current to be measured.
5. The FOCT drift fault signature extraction method as set forth in claim 1, wherein: the alpha is 0.5.
6. The FOCT drift fault signature extraction method as set forth in claim 1, wherein: the method for calculating sample entropy by the intrinsic scale component and the residual signal comprises the following steps:
(3-1) decomposing the intrinsic scale component and the residual signal into a vector sequence X (i) with a one-dimensional array of m
X(i)＝[y1,y2,...,yi...,ym],i＝1,2,...,m
Y i is the ith one-dimensional array of the decomposition;
(3-2) defining a distance d (i, j) between vectors y i and y j, where j=1, 2,..m (i+.j)
d(i,j)＝|yi-yj|
(3-3) Setting a threshold r, counting the number b of distances d (i, j) smaller than r, and then comparing the number b with the total number m of distances, and recording asI.e.
Wherein: i=1, 2,. -%, m;
(3-4) B m (r) is Average value of (2)
(3-5) Increasing the dimension to m+1, repeating the steps (3-1) to (3-4) to obtain B m+1 (r)
(3-6) Sample entropy formula of intrinsic scale component, residual signal as follows:
7. the FOCT drift fault signature extraction method as set forth in claim 1, wherein: the threshold is set to 1.
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