CN107525671B - Method for separating and identifying compound fault characteristics of transmission chain of wind turbine generator - Google Patents
Method for separating and identifying compound fault characteristics of transmission chain of wind turbine generator Download PDFInfo
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Abstract
A method for separating and identifying composite fault characteristics of a wind power transmission chain comprises the following steps: obtaining a vibration signal using an acceleration sensor; decomposing the acquired vibration signals in a full frequency range by utilizing multi-wavelet packet transformation; selecting single signals meeting the requirements to reconstruct respectively by taking the permutation entropy as an evaluation index, and completing the noise reduction and the compound fault separation of the signals; and processing the reconstructed signal by adopting an energy operator demodulation method to complete the identification of the fault information.
Description
Technical Field
The invention relates to a method for separating compound fault characteristics of a transmission chain of a wind turbine generator.
Background
Along with the rapid development of wind power generation technology, the function of wind power equipment becomes strong, the volume becomes large, the intelligence degree becomes high, the corresponding operation maintenance cost also increases continuously, along with the increase of service time of a wind turbine generator, the failure rate is increased remarkably, data statistics proves that the failure mainly concentrates on parts such as a wind power electrical control system, a transmission system, blades and the like, once the failure occurs, the machine halt is caused, and huge economic loss is brought, wherein the failure of the transmission system causes the longest machine halt maintenance time, so that the operation state of a transmission chain system of the wind turbine generator is necessary to be monitored and diagnosed.
In actual operation, multiple faults of wind power equipment often occur due to various reasons, and the operation condition of the wind power equipment can be changed due to the occurrence of one fault, so that related components are caused to be in fault. The gearbox is used as an important transmission component of the wind power generation equipment, and when the gearbox operates in a fault state, most of collected signals are modulated fault signals, because when a fault occurs, the fault signals mainly represent that: the meshing frequency of the gear is modulated by the rotation frequency and the frequency multiplication of the shaft; when the bearing fails, the acquired vibration signal can be modulated by a periodic instantaneous impact pulse signal. Therefore, how to accurately and quickly extract fault characteristics is a key for diagnosing the running state of the wind power equipment, and the energy operator demodulation is used as a simple and quick demodulation method, so that the signal modulation phenomenon can be effectively solved. However, the energy operator demodulation method is susceptible to noise, and most of vibration signals actually obtained by engineering are complex amplitude modulation and frequency modulation signals and contain strong background noise, so that energy operator demodulation is directly performed, and all fault features are difficult to extract.
The chinese patent CN103900816A "a wind turbine generator system bearing fault diagnosis method" adopts a wavelet packet method to decompose vibration signals, adopts a soft threshold value to reduce noise of decomposed high-frequency coefficients, then performs signal reconstruction, performs wavelet packet three-layer decomposition on reconstructed signals to obtain energy of each frequency band of a third layer, and inputs the energy as a feature vector into a BP neural network to realize wind turbine generator system bearing fault diagnosis. The patent adopts a wavelet packet method to realize the noise reduction of signals and the accurate judgment of whether the signals have faults, but cannot really realize the separation and the identification of modulated composite faults. Chinese patent CN102937522A, a composite fault diagnosis and system for gear boxes, uses a dual-tree complex wavelet transform method to decompose signals to obtain multiple sub-bands, and uses an energy operator method to analyze signals of each sub-band respectively to extract fault characteristics. The document 'application of an LMD (local mean decomposition) method based on adaptive multi-scale morphology and a Teager energy operator method in bearing fault diagnosis' proposes that LMD (local mean decomposition) is adopted to decompose into a plurality of sub-band signals, multi-scale morphological filtering is utilized to process each sub-band, and energy operator demodulation is adopted after processing to obtain bearing fault characteristic frequency.
Disclosure of Invention
The invention aims to overcome the defects in the prior art and provides a method for separating and identifying the composite fault characteristics of a transmission chain of a wind turbine generator.
According to the invention, a multi-wavelet-packet algorithm is introduced, and the multi-wavelet-packet decomposition method can finely decompose signals in all frequency ranges. The invention utilizes the characteristic that the multi-wavelet contains a plurality of time-frequency characteristic base functions, obtains comprehensive fault information through the matching of the base functions and fault characteristics, and realizes the identification and diagnosis of the fault. The invention decomposes the modulation signal containing the composite fault by utilizing the multi-wavelet packet transformation, selects the single-branch signal which meets the condition to carry out the characteristic reconstruction by taking the permutation entropy as the evaluation index, demodulates and analyzes the reconstructed fault signal by applying the energy operator demodulation method, obtains the demodulation frequency spectrum, extracts the fault characteristic and realizes the separation and the diagnosis of the fault characteristic.
The method comprises the steps of separating the characteristics of signals by adopting a multi-wavelet packet method, selecting a single-branch signal to carry out multi-wavelet packet reconstruction, demodulating an energy operator, identifying fault characteristics and the like, and specifically comprises the following steps:
the method comprises the following steps: acquiring a vibration signal x (t) of a transmission chain of a wind turbine generator by adopting a vibration acceleration sensor, wherein t represents the time corresponding to the acquired signal;
step two: decomposing the vibration signal x (t) acquired in the step 1 by adopting a multi-wavelet packet method, which comprises the following specific steps:
(1) preprocessing a vibration signal x (t) by a plurality of wavelet packets;
because the vibration signal x (t) is a one-dimensional signal and the multi-wavelet packet generally comprises a plurality of scale functions and wavelet functions, the vibration signal x (t) is subjected to multi-wavelet packet preprocessing by adopting a repeated sampling method to obtain a two-dimensional multi-wavelet packet preprocessing signal x (t)1And (t), wherein t represents the time corresponding to the signal acquisition.
(2) Preprocessing signal x for two-dimensional multi-wavelet packet1(t) performing multi-wavelet packet decomposition;
the multi-wavelet packet decomposition formula is as follows:
in the formula, sj,kScale factor representing the j-th decomposition, dj,kThe multi-wavelet packet coefficients representing the j-th layer decomposition,Hn-2ksubjecting the signal to a multi-wavelet-packet decomposition low-pass filter coefficient, Gn-2kPerforming a multi-wavelet-packet decomposition high-pass filter coefficient for the signal, j being the number of layers of the multi-wavelet-packet decomposition, sj-1,nScale factor representing the j-1 th layer decomposition, dj-1,nThe multi-wavelet packet coefficient representing the j-1 layer decomposition, n is the number of sampling points, and k is the number of sampling points k of the j layer coefficient of the multi-wavelet packet decomposition, wherein the number of the sampling points k is 0,1, … and n-1;
and simultaneously decomposing the scale function and the multi-wavelet packet function, wherein the third layer of decomposition obtains 4 groups of scale functions and 4 groups of multi-wavelet packet functions, and 16 single signals are obtained in total.
Step three: respectively analyzing the single-branch signals obtained in the step (2) by taking the permutation entropy as an evaluation index, and selecting the single-branch signals with rich characteristic information to carry out multi-wavelet-packet single-branch reconstruction to obtain multi-wavelet-packet single-branch reconstruction signals;
(1) firstly, solving the permutation entropy, and specifically comprising the following steps:
1) reconstructing the phase space of a signal
Assuming a discrete time sequence { x (t), t ═ 1, 2.... times, N }, it is phase-space reconstructed, resulting in:
in the formula: y is the reconstructed phase space
N is the length of discrete time data
m is the embedding dimension
τ is a time delay
z is the number of reconstructed components, and z is N-m +1
x (a) is the a row component of the reconstruction matrix
a is any line component of the reconstructed phase space, a is 1,2
2) Recombining original signals
The row component Y (a:) in the matrix is considered as one reconstruction component, having a reconstruction components, a ═ N-m +1, x (a + τ) for each element x (a), x (a + τ) in Y (a:)]Rearranged from small to large, i1,i2,...,ibIndex positions representing columns in which elements are rearranged, and after rearrangement, a unique group of symbol sequences are provided: s (l) ═ i1,i2,...,imWhere l is 1,2, and z, the probability of each sort is P by statistical calculation1,P2,...,Pe，e≤m！；
3) Solving the value of permutation entropy
Permutation entropy H of a discrete random time series { x (t), t ═ 1,2pDefined by the formula:
a＝1,2,…,z，paprobability of the a-th ordering;
4) normalized permutation entropy
And (3) carrying out normalization processing on the data: hP＝HP(m)/ln(m！)。
(2) Selecting single signals with rich characteristic information:
according to the Chebyshev inequality, the threshold value of the permutation entropy is set to be 0.79, the single-branch signal with the permutation entropy larger than 0.79 is used as a reconstruction signal, and two single-branch signals with the permutation entropy larger than 0.79 are selected by the data.
Step four: reconstructing the signal, adopting the multi-wavelet packet to reconstruct the single-branch signal, obtaining the reconstructed signal ya(t), wherein a represents the number of reconstructed signals, and t represents the time corresponding to the signals;
the multi-wavelet packet reconstruction is used as an inverse transformation process of decomposition, and the multi-wavelet packet reconstruction is carried out on the selected single-branch signal with rich characteristic information, and the specific formula is as follows:
k＝0,1,…,N-1
in the formula, sj,nRepresents a scale factor, dj,nRepresenting wavelet coefficients, Hk-2nReconstruction of the Low pass Filter coefficients, G, for the Signal pair signals with multiple wavelet packetsk-2nReconstructing the high-pass filter coefficients for the multi-wavelet packet reconstruction of the signal, j beingWavelet reconstruction layer number, N is sampling point number, sj-1,nScale factor representing the j-1 th layer decomposition, dj-1,nThe wavelet coefficients representing the j-1 layer decomposition, n is the number of sampling points, and k is the number of sampling points k of the j layer coefficient of the multi-wavelet-packet decomposition, which is equal to 0,1, … and n-1.
Step five: demodulation analysis, namely demodulating the reconstructed signal y obtained in the step 4 by adopting an energy operator methoda(t) obtaining corresponding demodulation spectrum characteristics;
the energy operator demodulation method comprises the following steps:
(1) for the reconstructed signal ya(t) defining its energy operator ψwComprises the following steps:
wherein t represents the time corresponding to the signal,
to reconstruct the signal ya(t) the first and second order differentials are obtained for time t.
(2) And (3) solving the instantaneous amplitude and the instantaneous frequency of the amplitude modulation frequency modulation time signal by adopting an energy operator:
wherein t represents the time corresponding to the signal, namely the time corresponding to the transmission chain of the wind turbine generator, v (t) is the instantaneous amplitude, and xuIs the instantaneous frequency.
Step six: comparing the demodulation spectrum characteristic frequency obtained in the step 5 with a fault characteristic frequency in the running process of the wind turbine generator transmission chain, and judging the running state of the wind turbine generator transmission chain, wherein the calculation formulas of the bearing fault frequency and the gear fault frequency are as follows:
(1) bearing failure frequency:
1) outer ring fault frequency fW
2) Inner ring failure frequency fN
3) Frequency of rolling element failure fG
4) Cage failure frequency fB
In the formula: d0Is the diameter of the rolling body; d is the pitch circle diameter; alpha is the nominal contact angle of the bearing; f. ofrIs the rotational speed frequency of the bearing; and o is the number of rolling elements of the bearing.
(2) Frequency of gear failure:
frequency of engagement fM
fM＝f1U1＝f2U2
In the formula: f is frIs the rotational speed frequency of the gear; u is the number of gear teeth; n gear rotating speed, with the unit of r/min; f. of1The rotating speed frequency of the driving wheel; f. of2Is the driven wheel rotational speed frequency; u shape1The number of teeth of the driving wheel; u shape2The number of teeth of the driven wheel.
Compared with the prior art, the method provided by the invention has the advantages that the complex wind power transmission structure is fully considered, and the problem of multi-fault frequency modulation is easily caused, and the multi-wavelet packet algorithm and the energy operator demodulation algorithm are combined to diagnose the compound fault of the wind power generator set transmission chain. The energy operator demodulation method is a simple and rapid demodulation method, can effectively solve the signal modulation phenomenon, and is easily influenced by noise due to the energy operator demodulation method. The multi-wavelet-packet transformation is used as an efficient and accurate decomposition method, the characteristics of orthogonality, tight support, symmetry, high-order vanishing moment and the like can be simultaneously met compared with wavelet-packet analysis, the problem that an energy operator demodulation method is easily affected by background noise can be effectively solved, the concept of the arrangement entropy is introduced, the arrangement entropy is used as an evaluation index of signals to quantitatively and quickly determine single-branch signals containing fault information, and scientific basis is provided for selection of single-branch reconstruction signals. In addition, the invention adopts the multi-wavelet packet to reconstruct the single branch signal, and the multi-wavelet packet single branch reconstruction can realize the signal noise reduction and complete the characteristic separation of the composite fault.
Drawings
FIG. 1 is a general flow diagram of the present invention;
FIG. 2 is a flow chart of multi-wavelet packet feature separation;
FIG. 3 is a flow chart of a multi-wavelet packet single branch reconstruction;
fig. 4 is a diagram of a result of identifying a composite fault by separately adopting an energy operator demodulation algorithm, fig. 4a is a time domain diagram, and fig. 4b is a demodulation spectrogram;
fig. 5 is a result graph of composite fault identification by using the present invention, fig. 5a is a time domain graph of a single-branch reconstructed signal 1, fig. 5b is a corresponding demodulation spectrogram, fig. 5c is a time domain graph of a single-branch reconstructed signal 2, and fig. 5d is a corresponding demodulation spectrogram.
Detailed Description
The invention is further described below with reference to the accompanying drawings and the detailed description.
As shown in fig. 1, the method for separating and identifying composite fault features of the present invention comprises the following steps:
the method comprises the following steps: acquiring a vibration signal x (t) of a transmission chain of a wind turbine generator by using a vibration acceleration sensor, wherein t represents the time corresponding to the signal;
step two: decomposing the vibration signal x (t) by using a multi-wavelet packet method, wherein the process is shown in fig. 2 and specifically includes the following steps:
(1) performing multi-wavelet preprocessing on the vibration signal x (t);
because the signal x (t) is a one-dimensional signal and the multi-wavelet generally comprises a plurality of scale functions and wavelet functions, the multi-wavelet preprocessing is carried out on the vibration signal x (t) by adopting a repeated sampling method to obtain a two-dimensional multi-wavelet preprocessed signal x (t)1(t), t represents the time corresponding to the signal;
(2) for x1(t) performing multi-wavelet packet decomposition;
the multi-wavelet decomposition formula is as follows:
k＝0,1,…,n-1
in the formula, sj,kRepresents a scale factor, dj,kRepresenting wavelet coefficients, Hn-2kSubjecting the signal to a multi-wavelet decomposition low-pass filter coefficient, Gn-2kPerforming multi-wavelet decomposition on the signal, wherein j is the number of wavelet decomposition layers, and n is the number t of sampling points representing the time corresponding to the vibration signal of the transmission chain of the wind turbine generator; in the formula, sj,kScale factor representing the j-th decomposition, dj,kWavelet coefficients representing the j-th decomposition, Hn-2kSubjecting the signal to a multi-wavelet decomposition low-pass filter coefficient, Gn-2kPerforming a multi-wavelet decomposition of the high-pass filter coefficients for the signal, j being the number of wavelet decomposition layers, sj-1,nScale factor representing the j-1 th layer decomposition, dj-1,nThe wavelet coefficients representing the j-1 layer decomposition are obtained, n is the number of sampling points, and k is the number of sampling points k of the j layer coefficients of the multi-wavelet decomposition, wherein k is 0,1, … and n-1;
and decomposing the scale function and the wavelet function at the same time, wherein the third layer of decomposition obtains 4 groups of scale functions and 4 groups of wavelet functions, and 16 single signals are obtained in total.
Step three: respectively analyzing the single-branch signals obtained in the step (2) by taking the permutation entropy as an evaluation index, and selecting the single-branch signals meeting the conditions to perform multi-wavelet packet single-branch reconstruction to obtain single-branch reconstructed signals;
(1) firstly, solving the permutation entropy, and specifically comprising the following steps:
1) reconstructing the phase space of a signal
Assuming a discrete time sequence { x (t), t ═ 1, 2.... times, N }, it is phase-space reconstructed, resulting in:
in the formula: y is the reconstructed phase space
N is the length of discrete time data
m is the embedding dimension
τ is a time delay
z is the number of reconstructed components, and z is N-m + 1
x (a) is the a row component of the reconstruction matrix
a is any line component of the reconstructed phase space, a is 1,2
2) Recombining original signals
The row component Y (j,: in the matrix) is considered as one reconstruction component, there are z reconstruction components, z is N-m +1, x (a + τ) for each element [ x (a), x (a + τ) ], x (a + (m-1) τ) in Y (a,: in total)]Rearranged from small to large, i1，i2，...，inIndex positions representing columns in which elements are rearranged, and after rearrangement, a unique group of symbol sequences are provided: s (l) ═ i1,i2,...,imWhere l is 1,2, and z, the probability of each sort is P by statistical calculation1,P2,...,Pe，e≤m！；
3) Solving the value of permutation entropy
Permutation entropy H of a discrete random time series { x (t), t ═ 1,2pDefined by the formula:
a＝1,2,…,z，paprobability of the a-th ordering;
4) normalized permutation entropy
And (3) carrying out normalization processing on the data: hP＝HP(m)/ln(m！)。
(2) Selecting single signals with rich characteristic information:
setting a threshold value of the permutation entropy as 0.79 according to the Chebyshev inequality, using the single-branch signal with the permutation entropy larger than 0.79 as a reconstruction signal, and selecting two single-branch signals with the permutation entropy larger than 0.79 according to the data;
and determining LLH1 and LHH2 as reconstructed signals.
Step four: and reconstructing the single-branch signal. Determining LLH1 and LHH2 as reconstruction signals by taking the permutation entropy as an evaluation index, reconstructing a single branch signal by taking a multi-wavelet packet reconstruction principle as guidance, and performing multi-wavelet post-processing after reconstruction is completed, wherein the flow is shown in FIG. 3, and the specific calculation formula is as follows.
k＝0,1,…,N-1
In the formula, sj,nRepresents a scale factor, dj,nRepresenting wavelet coefficients, Hk-2nMulti-wavelet reconstruction of low-pass filter coefficients for signal pairs, Gk-2nFor multi-wavelet reconstruction of high-pass filter coefficients for a signal, j is the number of wavelet reconstruction layers, N is the number of sampling points, sj-1,nScale factor representing the j-1 th layer decomposition, dj-1,nThe wavelet coefficients representing the j-1 layer decomposition, n is the number of sampling points, and k is the number of sampling points k of the j-layer coefficients of the multi-wavelet decomposition, which is equal to 0,1, … and n-1.
Step five: demodulation analysis, adopting an energy operator demodulation method to analyze the reconstructed signal y obtained in the step 4a(t) obtaining corresponding demodulation spectrum characteristics;
the energy operator demodulation method comprises the following steps:
(1) for the reconstructed signal ya(t) determination ofDefining its energy operator psiwComprises the following steps:
wherein t represents the time corresponding to the signal,
as a time signal ya(t) the first and second order differentials are obtained for time t.
(2) And (3) solving the instantaneous amplitude and the instantaneous frequency of the amplitude modulation frequency modulation time signal by adopting an energy operator:
where t represents the time corresponding to the signal, v (t) is the instantaneous amplitude, xuIs the instantaneous frequency.
Step six: determining faults
Comparing the demodulation spectrum frequency characteristics obtained in the step 5 with fault characteristic frequency fault frequency in the running process of the wind turbine generator transmission chain, and judging the running state of the wind turbine generator, wherein the running state is identified by the bearing fault of the wind turbine generator transmission chain:
bearing failure frequency:
1) outer ring fault frequency fW
2) Inner ring failure frequency fN
3) Frequency of rolling element failure fG
4) Cage failure frequency fB
In the formula: d0Is the diameter of the rolling body; d is the pitch circle diameter; alpha is the nominal contact angle of the bearing; f. ofrIs the rotational speed frequency of the bearing; and o is the number of rolling elements of the bearing.
TABLE 1 failure characteristic frequency of rolling bearings
Fig. 4b is the frequency of the fault of fig. 4a obtained by processing with an energy operator demodulation method, according to the operation of the known device: and the inner ring fault frequency is 121.9Hz, and the bearing inner ring fault is judged. Due to the defect of energy operator demodulation, the method of the invention is adopted to process the vibration signal of the wind turbine generator transmission chain, and the result is shown in fig. 5a, fig. 5b, fig. 5c and fig. 5 d: the outer ring fault frequency is 76.88Hz, which shows that the bearing has the composite fault of the inner ring and the outer ring at the same time, and proves that the invention can realize the separation and identification of the composite fault.
Claims (3)
1. A method for separating and identifying compound fault characteristics of a transmission chain of a wind turbine generator is characterized by comprising the following steps: the method adopts a multi-wavelet packet method to separate the characteristics of signals, selects a single branch signal to carry out multi-wavelet packet reconstruction, demodulates and identifies fault characteristics by an energy operator, and specifically comprises the following steps:
the method comprises the following steps: acquiring a vibration signal x (t) of a transmission chain of a wind turbine generator by using a vibration acceleration sensor, wherein t represents the time corresponding to the signal;
step two: decomposing the vibration signal x (t) acquired in the step one in a full frequency range by adopting a multi-wavelet-packet method;
step three: analyzing the single-branch signals obtained in the step two by taking the permutation entropy as an evaluation index, and selecting the single-branch signals with rich characteristic information to carry out multi-wavelet packet single-branch reconstruction;
step four: respectively reconstructing the selected single branch signals by adopting a multi-wavelet packet method to obtain multi-wavelet packet single branch reconstructed signals ya(t), wherein a is 1,2 …, a represents the number of reconstructed signals, and t represents the time corresponding to the signals;
step five: demodulation analysis, namely demodulating the reconstructed signal y obtained in the step three by adopting an energy operator methoda(t) obtaining corresponding demodulation spectrum characteristics;
step six: comparing the characteristic frequency of the demodulation spectrum obtained in the step four with the fault characteristic frequency in the running process of the transmission chain of the wind turbine generator, and judging the running state of the transmission chain of the wind turbine generator;
in the second step, a method for decomposing the vibration signal x (t) acquired in the first step by adopting a multi-wavelet packet method is as follows:
(1) preprocessing a vibration signal x (t) by a plurality of wavelet packets;
performing multi-wavelet packet preprocessing on the vibration signal x (t) by adopting a repeated sampling method to obtain a two-dimensional multi-wavelet packet preprocessing signal x1(t), t represents the time corresponding to the signal;
(2) preprocessing signal x for two-dimensional multi-wavelet packet1(t) performing multi-wavelet packet decomposition;
the multi-wavelet packet decomposition formula is as follows:
in the formula, sj,kScale factor representing the j-th decomposition, dj,kMultiple wavelet packet coefficients, H, representing the j-th decompositionn-2kSubjecting the signal to a multi-wavelet-packet decomposition low-pass filter coefficient, Gn-2kPerforming a multi-wavelet-packet decomposition high-pass filter coefficient for the signal, j being the number of multi-wavelet-packet decomposition layers,sj-1,nscale factor representing the j-1 th layer decomposition, dj-1,nThe multi-wavelet packet coefficients representing the j-1 layer decomposition are n, k is the number of sampling points of the multi-wavelet packet decomposition j, and k is 0,1, …, n-1;
decomposing the scale function and the multi-wavelet packet function at the same time, wherein the third layer of decomposition obtains 4 groups of scale functions and 4 groups of multi-wavelet packet functions, and 16 single signals are obtained in total;
the third step is to analyze the single branch signals obtained in the second step by taking the permutation entropy as an evaluation index, select the single branch signals with rich characteristic information to carry out multi-wavelet packet single branch reconstruction, and obtain the multi-wavelet packet single branch reconstruction signals, wherein the method comprises the following steps:
(1) firstly, solving permutation entropy:
1) reconstructing the phase space of a signal
Assuming a discrete time sequence { x (t), t ═ 1, 2.... times, N }, it is phase-space reconstructed, resulting in:
in the formula: y is the reconstructed phase space, N is the discrete-time data length, m is the embedding dimension, τ is the time delay, z is the number of reconstructed components, z is N-m +1, x (a) is the row a component of the reconstruction matrix, a is any row component of the reconstructed phase space, a is 1, 2.
2) Recombining original signals
The row component Y (a,: in the matrix is considered as one reconstruction component, there are z reconstruction components, x (a + τ), and x (a + (m-1) τ) for each element [ x (a), x (a + τ) in Y (a,: in.: terms, x (a + (m-1) τ)]Rearranged from small to large, i1,i2,...,ibIndex positions representing columns in which elements are rearranged, and after rearrangement, a unique group of symbol sequences are provided: s (l) ═ i1,i2,...,imWhere l is 1,2, and z, the probability of each sort is P by statistical calculation1,P2,...,Pe，e≤m！；
3) Solving the value of permutation entropy
Permutation entropy H of a discrete random time series { x (t), t ═ 1,2pDefined by the formula:
paprobability of the a-th ordering;
4) normalized permutation entropy
And (3) carrying out normalization processing on the data: hP＝HP(m)/ln(m！)；
(2) Selecting single signals with rich characteristic information:
setting a threshold value of the permutation entropy as 0.79 according to the Chebyshev inequality, and taking the single branch signal with the permutation entropy larger than 0.79 as a reconstruction signal;
the method for reconstructing the selected signal in the fourth step is as follows:
and C, performing multi-wavelet packet reconstruction on the single-branch signal with rich characteristic information selected in the step three, wherein a specific formula is as follows:
in the formula, sj,nRepresents a scale factor, dj,nRepresenting the multi-wavelet packet coefficient, Hk-2nReconstruction of the Low pass Filter coefficients, G, for the Signal pair signals with multiple wavelet packetsk-2nFor multi-wavelet packet reconstruction of the signal the high-pass filter coefficient, j is the number of reconstructed layers of the multi-wavelet packet, sj-1,nScale factor representing the j-1 th layer decomposition, dj-1,nThe wavelet coefficients representing the j-1 layer decomposition, n is the number of sampling points, and k is the number of sampling points k of the j-layer coefficients of the multi-wavelet decomposition, which is equal to 0,1, … and n-1.
2. The method for separating and identifying the compound fault characteristics of the transmission chain of the wind turbine generator set according to claim 1, wherein the method comprises the following steps: step five, demodulating the reconstructed signal y obtained in step four by adopting an energy operator methoda(t), the method for obtaining the corresponding demodulation spectrum characteristics is as follows:
the energy operator demodulation method comprises the following steps:
(1) for the reconstructed signal ya(t) defining its energy operator ψwComprises the following steps:
wherein t represents the time corresponding to the reconstructed signal,
to reconstruct the signal ya(t) obtaining a first order differential and a second order differential of the time t;
(2) and (3) solving the instantaneous amplitude and the instantaneous frequency of the amplitude modulation frequency modulation time signal by adopting an energy operator:
wherein t represents the time corresponding to the reconstructed signal, i.e. the time corresponding to the transmission chain of the wind turbine generator, v (t) is the instantaneous amplitude, xuIs the instantaneous frequency.
3. The method for separating and identifying the compound fault characteristics of the transmission chain of the wind turbine generator set according to claim 1, wherein the method comprises the following steps: and sixthly, comparing the characteristic frequency of the demodulation spectrum obtained in the fifth step with the fault characteristic frequency of the wind turbine generator in the running process, and judging the running state of the wind turbine generator.
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