CN113625101A - Traveling wave signal processing method based on fruit fly algorithm and stochastic resonance - Google Patents
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Abstract
The invention discloses a traveling wave signal processing method based on fruit fly algorithm and stochastic resonance, which comprises the steps that after a line fault occurs, a system main station collects traveling wave signals in real time; processing the traveling wave signal by using a self-adaptive stochastic resonance model based on a drosophila algorithm to obtain a traveling wave signal with a high signal-to-noise ratio; and extracting fault characteristic information of the traveling wave signal with high signal-to-noise ratio by using a traveling wave extraction method. The invention effectively improves the signal-to-noise ratio of the traveling wave signal under strong noise interference, and is convenient for accurately detecting fault characteristic information contained in the traveling wave signal.
Description
Technical Field
The invention relates to a traveling wave signal processing method based on a drosophila algorithm and stochastic resonance, and belongs to the technical field of signal processing.
Background
With the development of economy, the scale of an electric power system is gradually increased, the network structure is gradually complicated, and the requirement of a user on power supply stability is higher and higher. Accurate fault location becomes an important guarantee for rapidly removing faults and improving the transient stability of the system. The traveling wave positioning method is widely applied to positioning the faults of the power distribution network. The accuracy of the detection of the traveling wave signal directly affects the accuracy of the fault traveling wave positioning and the reliability of the traveling wave protection, and the accurate detection technology of the traveling wave signal becomes the key of the traveling wave positioning and protection technology development.
The amplitude of the traveling wave of the power distribution network is far smaller than that of the power transmission network due to the low voltage level of the power distribution network, the difference of equivalent capacitance of capacitive equipment and the like. When the fault grounding resistance is high or the fault initial phase angle is small, the generated fault traveling wave signal is weak, and the traveling wave signal is submerged by noise, which undoubtedly increases the difficulty of accurately measuring the traveling wave signal. At present, a plurality of scholars propose to extract traveling wave signals by using methods such as wavelet transformation, Hilbert-Huang change, variational modal decomposition and the like, but the method has certain limitation under strong noise interference. And the stochastic resonance can generate a synergistic effect between noise and characteristic signals, reduce the noise, increase the energy of fault characteristic signals and facilitate the detection of traveling wave signals. The traveling wave signal is a transient and abrupt signal, stable transition cannot be formed in a stochastic resonance system, only one steady-state point is provided, and the traveling wave signal is a typical monostable stochastic resonance system. The monostable stochastic resonance system is a special bistable stochastic resonance model and still has the characteristic of resonance in a trap. However, in the conventional stochastic resonance detection system, system parameters are mostly set manually, have a certain contingency, and the optimal parameter values of the system cannot be obtained.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provides a travelling wave signal processing method based on a fruit fly algorithm and stochastic resonance. In order to achieve the purpose, the invention provides a traveling wave signal processing method based on a fruit fly algorithm and stochastic resonance, which comprises the following steps:
after a line fault occurs, a system main station collects traveling wave signals in real time;
processing the traveling wave signal by using a self-adaptive stochastic resonance model based on a drosophila algorithm to obtain a traveling wave signal with a high signal-to-noise ratio;
and extracting fault characteristic information of the traveling wave signal with high signal-to-noise ratio by using a traveling wave extraction method.
Preferably, the construction of the adaptive stochastic resonance model based on the drosophila algorithm comprises:
constructing a monostable stochastic resonance model, wherein the formula is as follows:
in the formula: y (t) is a given input signal; δ (t) is a noise signal; u (t) is a traveling wave signal without noise; a and b are set model parameters; x is the monostable stochastic resonance model output signal,
the derivative of the output signal of the monostable stochastic resonance model with respect to time t;
and solving x by using a fourth-order Runge Kutta method under the condition that y (t), a and b and a sampling frequency step length h are known.
Preferably, the construction of the adaptive stochastic resonance model based on the drosophila algorithm comprises:
carrying out normalization scale transformation on the monostable stochastic resonance model to enable the monostable stochastic resonance model to meet stochastic resonance conditions, wherein the monostable stochastic resonance model after the transformation is as follows:
in the formula, z is an output signal of the monostable stochastic resonance model after the normalization scale transformation;
for a given input signal y (t), different model parameters a and b produce different stochastic resonance effects, and in order to optimize the stochastic resonance effects, the parameters a and b are adaptively adjusted based on a drosophila algorithm.
Preferentially, parameters a and b are adaptively adjusted based on a drosophila algorithm, comprising:
self-adaptive adjustment parameters a and b based on a fruit fly algorithm comprise:
1) setting a0And b0The initial values are all sampling frequencies, and the initial value L of the step length L is assigned0J is 1, a positive integer F, i is 1 and a constant C;
2) setting the food searching direction of the fruit fly individuals by utilizing the smell as random, the number n of the fruit flies and the movable radius of the fruit fly individuals by utilizing the smell to search the food, and establishing an updating formula:
wherein i is 1,2, …, n; j is 1,2, …, m, m is the set iteration number; a isjiThe value of the parameter a corresponding to the ith fruit fly after the jth iterative optimization is obtained; bjiThe value of the parameter b corresponding to the ith fruit fly after the jth iterative optimization; rand (n,1) represents that an n x 1 dimensional matrix is randomly generated;
3) sequentially substituting i-1, 2, …, n into formula (3), and obtaining a total of n (a) based on formula (3)jn,bjn)；
Will be n in total (a)jn,bjn) Inputting the signals into a formula (2) in sequence, solving the formula (2) based on a fourth-order Runge Kutta method to obtain n output signals z in sequence, inputting the signal-to-noise ratio of the ith output signal z and the signal-to-noise ratio of the ith input signal y into the formula (4), and calculating based on the formula (4) to obtain n corresponding stochastic resonance signal-to-noise ratio gains SNRI:
wherein SNR isoutFor the signal-to-noise ratio, SNR, of the output signal zinIs the signal-to-noise ratio of the input signal y;
4) recording the maximum value SNRI among n stochastic resonance signal-to-noise ratio gainsL,maxAnd model parameters a and b corresponding to the maximum value;
5) increasing the value of L by C, and repeating the steps 1) to 4) until L reaches a set maximum value Lmax，Lmax＝L0+ FXC to obtain a total of F SNRIsL,max；
Noting the maximum SNRI of the F stochastic resonance signal-to-noise ratio gainsL,maxAnd model parameters a and b corresponding to the maximum value;
6) a in the formula (3)0Updating the model parameter a corresponding to the maximum value, and using b in the formula (3)0Updating the model parameter b corresponding to the maximum value;
7) adding 1 to the value of j, judging whether j is larger than m, if j is smaller than or equal to m, repeating the steps 2) -6), and if not, entering the step 8);
8) obtaining a total of m SNRIs based on step 7)L,maxAnd the SNRIL,maxObtaining m SNRIs based on formula (5) according to model parameters a and bL,maxMiddle-maximum SNRIL,max：
max(SNRIL0,max，SNRIL0+C,max，…，SNRILmax,max) (5)，
Updating a in formula (2) to the maximum SNRIL,maxCorresponding to the model parameter a, updating b in the formula (2) to the maximum SNRIL,maxCorresponding model parameters b.
Preferably, the traveling wave extraction method may be one of wavelet transform, hilbert-yellow transform, and variational modal decomposition.
Preferably, m is in the range of [1000,2000], C is 100, and L is in the interval of [1,1000 ].
Preferably, the traveling wave extraction method is one of wavelet transform, hilbert-yellow transform, and variational modal decomposition.
The invention achieves the following beneficial effects:
the invention adopts the fruit fly algorithm to optimize the stochastic resonance parameters, and then adopts the optimized stochastic resonance model to process the traveling wave signal to obtain the traveling wave signal with larger signal-to-noise ratio, thereby facilitating the accurate detection of the fault characteristic information of the traveling wave signal. The method realizes the self-adaptive selection of the stochastic resonance parameters and overcomes the difficulty that the stochastic resonance is limited due to the difficulty in the selection of the system parameters. The signal-to-noise ratio of the traveling wave signal is improved by utilizing the self-adaptive stochastic resonance based on the fruit fly algorithm, and the fault characteristic information of the traveling wave signal can be accurately detected conveniently.
Drawings
FIG. 1 is a flow chart of the present invention;
FIG. 2 is a waveform diagram of a traveling wave signal of a first section voltage of a line after a fault occurs under an ideal condition;
FIG. 3 is a waveform diagram of a voltage traveling wave signal after a strong noise interference is superimposed;
fig. 4 is a waveform diagram of a traveling wave signal after processing according to the present invention.
Detailed Description
The following examples are only for illustrating the technical solutions of the present invention more clearly, and the protection scope of the present invention is not limited thereby.
The traveling wave extraction method can use wavelet transformation, Hilbert-Huang change or variation modal decomposition in the prior art and the like. Solving x by using a four-order Runge Kutta method is prior art, extracting fault characteristic information of traveling wave signals with high signal-to-noise ratio by using a traveling wave extraction method, and solving z by using a four-order Runge Kutta method is prior art, and the processes are not elaborated in detail.
The traveling wave signal processing method based on the fruit fly algorithm and the stochastic resonance comprises the following steps:
after a line fault occurs, a system main station collects traveling wave signals in real time;
processing the traveling wave signal by using a self-adaptive stochastic resonance model based on a drosophila algorithm to obtain a traveling wave signal with a high signal-to-noise ratio;
and extracting fault characteristic information of the traveling wave signal with high signal-to-noise ratio by using a traveling wave extraction method.
Further, constructing a self-adaptive stochastic resonance model based on a drosophila algorithm, comprising the following steps:
constructing a monostable stochastic resonance model, wherein the formula is as follows:
in the formula: y (t) is a given input signal; δ (t) is a noise signal; u (t) is a traveling wave signal without noise; a and b are set model parameters; x is the monostable stochastic resonance model output signal,
the derivative of the output signal of the monostable stochastic resonance model with respect to time t;
and solving x by using a fourth-order Runge Kutta method under the condition that y (t), a and b and a sampling frequency step length h are known.
Further, constructing a self-adaptive stochastic resonance model based on a drosophila algorithm, comprising the following steps:
carrying out normalization scale transformation on the monostable stochastic resonance model to enable the monostable stochastic resonance model to meet stochastic resonance conditions, wherein the monostable stochastic resonance model after the transformation is as follows:
in the formula, z is an output signal of the monostable stochastic resonance model after the normalization scale transformation;
for a given input signal y (t), different model parameters a and b produce different stochastic resonance effects, and in order to optimize the stochastic resonance effects, the parameters a and b are adaptively adjusted based on a drosophila algorithm.
Further, parameters a and b are adaptively adjusted based on a drosophila algorithm, and the method comprises the following steps:
self-adaptive adjustment parameters a and b based on a fruit fly algorithm comprise:
1) is provided withA is fixed0And b0The initial values are all sampling frequencies, and the initial value L of the step length L is assigned0J is 1, a positive integer F, i is 1 and a constant C;
2) setting the food searching direction of the fruit fly individuals by utilizing the smell as random, the number n of the fruit flies and the movable radius of the fruit fly individuals by utilizing the smell to search the food, and establishing an updating formula:
wherein i is 1,2, …, n; j is 1,2, …, m, m is the set iteration number; a isjiThe value of the parameter a corresponding to the ith fruit fly after the jth iterative optimization is obtained; bjiThe value of the parameter b corresponding to the ith fruit fly after the jth iterative optimization; rand (n,1) represents that an n x 1 dimensional matrix is randomly generated;
3) sequentially substituting i-1, 2, …, n into formula (3), and obtaining a total of n (a) based on formula (3)jn,bjn)；
Will be n in total (a)jn,bjn) Inputting the signals into a formula (2) in sequence, solving the formula (2) based on a fourth-order Runge Kutta method to obtain n output signals z in sequence, inputting the signal-to-noise ratio of the ith output signal z and the signal-to-noise ratio of the ith input signal y into the formula (4), and calculating based on the formula (4) to obtain n corresponding stochastic resonance signal-to-noise ratio gains SNRI:
wherein SNR isoutFor the signal-to-noise ratio, SNR, of the output signal zinIs the signal-to-noise ratio of the input signal y;
4) recording the maximum value SNRI among n stochastic resonance signal-to-noise ratio gainsL,maxAnd model parameters a and b corresponding to the maximum value;
5) increasing the value of L by C, and repeating the steps 1) to 4) until L reaches a set maximum value Lmax，Lmax＝L0+ FXC to obtain a total of F SNRIsL,max；
Noting the maximum SNRI of the F stochastic resonance signal-to-noise ratio gainsL,maxAnd model parameters a and b corresponding to the maximum value;
6) a in the formula (3)0Updating the model parameter a corresponding to the maximum value, and using b in the formula (3)0Updating the model parameter b corresponding to the maximum value;
7) adding 1 to the value of j, judging whether j is larger than m, if j is smaller than or equal to m, repeating the steps 2) -6), and if not, entering the step 8);
8) obtaining a total of m SNRIs based on step 7)L,maxAnd the SNRIL,maxObtaining m SNRIs based on formula (5) according to model parameters a and bL,maxMiddle-maximum SNRIL,max：
max(SNRIL0,max，SNRIL0+C,max，…，SNRILmax,max)(5)，
Updating a in formula (2) to the maximum SNRIL,maxCorresponding to the model parameter a, updating b in the formula (2) to the maximum SNRIL,maxCorresponding model parameters b.
Further, the traveling wave extraction method may be one of wavelet transform, hilbert-yellow variation, and variational modal decomposition.
Further, m is [1000,2000], C is 100, and L is set to [1,1000 ].
Further, the traveling wave extraction method is one of wavelet transformation, hilbert-yellow variation and variation modal decomposition.
The method is implemented specifically as follows:
the first iteration: j is 1; calculating according to formula 3) to obtain model parameters respectively as (a)11,b11),(a12,b12),…,(a1n,b1n) Corresponding to n set fruit fly numbers.
The first fruit fly corresponds to
(a11,b11) The second fruit fly corresponds to (a)12,b12),
a11A0+ L (data in the first row and column of Rand (n, 1));
a12a0+ L (data in the second row and the first column of Rand (n, 1));
respectively substituting the values into a formula (2), solving the formula (2) by using a four-step Runge Kutta method to obtain n output signals z, and then calculating n corresponding signal-to-noise ratio gains; reserving the maximum value of the n signal-to-noise ratio gains and the corresponding model parameters; one signal-to-noise ratio gain corresponds to one output signal Z, one output signal Z corresponding to a pair of model parameters a and b. Adjusting the value of L, repeating the steps,
assuming that L takes 10 different values, 10 signal-to-noise gains will be preserved by calculation; then, the maximum noise ratio gain is reserved from the inside, and the large value is recorded as SNRI1.maxAnd the corresponding model parameters are retained.
Carrying out second iteration; j is 2, and the model parameters can be calculated according to the formula 3), and are (a) respectively21,b21),(a22,b22),…,(a2n,b2n) (subsequent repetition of the same steps); SNRI2.max
And repeating the same steps until the m time.
Once m calculations are made, the maximum gain of the m calculations, i.e., max (SNRI), is selected1.max,SNRI2.max,…,SNRIm.max)。
In a power distribution network simulation model with a certain ungrounded 10kv neutral point, line parameters are as follows: line length L14 km, positive sequence inductance Lm1＝0.9337×10-3H/km, positive sequence capacitance Cm1＝1274×10-9F/km, positive sequence resistance r1Calculate the wave velocity v at 0.01273 Ω/km1＝2.8994×105km/s, and the sampling frequency is 10 MHZ;
a phase-A grounding fault is judged and identified at a position 5km away from the head end of the line at 0.041s based on a fault identification method in the prior art, and the transition resistance is 10 kilohms. The 60 μ s post-fault voltage waveform was taken. Fig. 2 shows the waveform of the traveling wave signal at the first section of the line under an ideal condition. As shown in fig. 3, in order to simulate strong noise interference, if noise occurs, the traveling wave signal is submerged in the noise signal due to its small amplitude; as can be seen from the figure, the traveling wave signal is completely annihilated by the interference of strong noise, so that it cannot be accurately identified. Fig. 2 (c) is a waveform obtained by processing the acquired traveling wave signal using an adaptive stochastic resonance method based on the drosophila algorithm. Comparing fig. 3 and 4, it can be seen intuitively that the signal-to-noise ratio of the traveling wave signal is significantly improved after the self-adaptive stochastic resonance. The problem that when the traveling wave signal is extracted, the traveling wave signal cannot be accurately detected after annihilation due to strong noise interference is solved. The above description is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, several modifications and variations can be made without departing from the technical principle of the present invention, and these modifications and variations should also be regarded as the protection scope of the present invention.
Claims (7)
1. The method for processing the traveling wave signal based on the drosophila algorithm and the stochastic resonance is characterized by comprising the following steps:
after a line fault occurs, a system main station collects traveling wave signals in real time;
processing the traveling wave signal by using a self-adaptive stochastic resonance model based on a drosophila algorithm to obtain a traveling wave signal with a high signal-to-noise ratio;
and extracting fault characteristic information of the traveling wave signal with high signal-to-noise ratio by using a traveling wave extraction method.
2. The traveling wave signal processing method based on fruit fly algorithm and stochastic resonance as claimed in claim 1,
constructing a self-adaptive stochastic resonance model based on a drosophila algorithm, comprising the following steps:
constructing a monostable stochastic resonance model, wherein the formula is as follows:
in the formula: y (t) is a given input signal; δ (t) is a noise signal; u (t) is a traveling wave signal without noise; a and b are set model parameters; x is the monostable stochastic resonance model output signal,
the derivative of the output signal of the monostable stochastic resonance model with respect to time t;
and solving x by using a fourth-order Runge Kutta method under the condition that y (t), a and b and a sampling frequency step length h are known.
3. The traveling wave signal processing method based on fruit fly algorithm and stochastic resonance as claimed in claim 2,
constructing a self-adaptive stochastic resonance model based on a drosophila algorithm, comprising the following steps:
carrying out normalization scale transformation on the monostable stochastic resonance model to enable the monostable stochastic resonance model to meet stochastic resonance conditions, wherein the monostable stochastic resonance model after the transformation is as follows:
in the formula, z is an output signal of the monostable stochastic resonance model after the normalization scale transformation;
for a given input signal y (t), different model parameters a and b produce different stochastic resonance effects, and in order to optimize the stochastic resonance effects, the parameters a and b are adaptively adjusted based on a drosophila algorithm.
4. The traveling wave signal processing method based on fruit fly algorithm and stochastic resonance as claimed in claim 3,
self-adaptive adjustment parameters a and b based on a fruit fly algorithm comprise:
1) setting a0And b0The initial values are all sampling frequencies, and the initial value L of the step length L is assigned0J is 1, a positive integer F, i is 1 and a constant C;
2) setting the food searching direction of the fruit fly individuals by utilizing the smell as random, the number n of the fruit flies and the movable radius of the fruit fly individuals by utilizing the smell to search the food, and establishing an updating formula:
wherein i is 1,2, …, n; j is 1,2, …, m, m is the set iteration number; a isjiThe value of the parameter a corresponding to the ith fruit fly after the jth iterative optimization is obtained; bjiThe value of the parameter b corresponding to the ith fruit fly after the jth iterative optimization; rand (n,1) represents that an n x 1 dimensional matrix is randomly generated;
3) sequentially substituting i-1, 2, …, n into formula (3), and obtaining a total of n (a) based on formula (3)jn,bjn)；
Will be n in total (a)jn,bjn) Inputting the signals into a formula (2) in sequence, solving the formula (2) based on a fourth-order Runge Kutta method to obtain n output signals z in sequence, inputting the signal-to-noise ratio of the ith output signal z and the signal-to-noise ratio of the ith input signal y into the formula (4), and calculating based on the formula (4) to obtain n corresponding stochastic resonance signal-to-noise ratio gains SNRI:
wherein SNR isoutFor the signal-to-noise ratio, SNR, of the output signal zinIs the signal-to-noise ratio of the input signal y;
4) recording the maximum value SNRI among n stochastic resonance signal-to-noise ratio gainsL,maxAnd model parameters a and b corresponding to the maximum value;
5) increasing the value of L by C, and repeating the steps 1) to 4) until L reaches a set maximum value Lmax，Lmax＝L0+ FXC to obtain a total of F SNRIsL,max；
Noting the maximum SNRI of the F stochastic resonance signal-to-noise ratio gainsL,maxAnd model parameters a and b corresponding to the maximum value;
6) a in the formula (3)0Updating the model parameter a corresponding to the maximum value, and using b in the formula (3)0Updating the model parameter b corresponding to the maximum value;
7) adding 1 to the value of j, judging whether j is larger than m, if j is smaller than or equal to m, repeating the steps 2) -6), and if not, entering the step 8);
8) obtaining a total of m SNRIs based on step 7)L,maxAnd the SNRIL,maxObtaining m SNRIs based on formula (5) according to model parameters a and bL,maxMiddle-maximum SNRIL,max：
max(SNRIL0,max，SNRIL0+C,max，…，SNRILmax,max) (5)，
Updating a in formula (2) to the maximum SNRIL,maxCorresponding to the model parameter a, updating b in the formula (2) to the maximum SNRIL,maxCorresponding model parameters b.
5. The fruit fly algorithm and stochastic resonance-based traveling wave signal processing method according to claim 5, wherein the traveling wave extraction method can be one of wavelet transform, Hilbert-Huang variation, and metamorphic modal decomposition.
6. The traveling wave signal processing method based on fruit fly algorithm and stochastic resonance as claimed in claim 4, wherein m is [1000,2000], C is 100, and L is [1,1000 ].
7. The fruit fly algorithm and stochastic resonance-based traveling wave signal processing method according to claim 5, wherein the traveling wave extraction method is one of wavelet transform, Hilbert-Huang variation, and metamorphic modal decomposition.
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