CN110703148A - A method and system for reconstruction of transformer vibro-acoustic signal using Hays matrix - Google Patents

Abstract

The embodiment of the present invention discloses a method and system for reconstructing a vibro-acoustic signal of a transformer using a Hays matrix. The method includes: step 1, inputting an actual measured vibro-acoustic signal sequence S; The transformer vibration and sound signal sequence S is reconstructed, and the reconstructed signal sequence is S _NEW ; specifically,

Among them, D is the Hays prediction matrix; W _OPT is the Hays optimal weight matrix; λ _OPT is the Hays optimal adjustment matrix; α is the Hays adjustment factor; μ is the Hays factor.

Description

A method and system for reconstruction of transformer vibro-acoustic signal using Hays matrix

technical field

The invention relates to the field of electric power, and in particular, to a method and system for reconstructing a transformer vibrating sound signal.

Background technique

With the rapid development of smart grid, the safe and stable operation of power equipment is particularly important. At present, it is more and more important and urgent to carry out the operation status detection of power equipment with ultra-high voltage and above voltage levels, especially the detection of abnormal status. As an important part of the power system, the power transformer is one of the most important electrical equipment in the substation, and its reliable operation is related to the security of the power grid. Generally speaking, the abnormal state of the transformer can be divided into iron core abnormality and winding abnormality. Iron core anomalies mainly manifest as iron core saturation, and winding anomalies usually include winding deformation, winding looseness, etc.

The basic principle of transformer abnormal state detection is to extract various characteristic quantities in the operation of the transformer, analyze, identify and track the characteristic quantities to monitor the abnormal operation state of the transformer. Detection methods can be divided into invasive detection and non-invasive detection according to the degree of contact; according to whether the shutdown detection can be divided into live detection and power failure detection; according to the type of detection, it can be divided into electrical measurement method and non-electric measurement method. In contrast, non-invasive detection has strong portability and is more convenient to install; live detection does not affect the operation of the transformer; non-electrical measurement method has no electrical connection with the power system, which is safer. The current common detection methods of transformer operation status include pulse current method and ultrasonic detection method for detecting partial discharge, frequency response method for detecting winding deformation, and vibration detection method for detecting mechanical and electrical faults. These detection methods mainly detect the insulation condition and mechanical structure condition of the transformer. Among them, the detection of the transformer vibration signal (vibration sound) is the most comprehensive, and it can respond to most of the transformer faults and abnormal states.

During the operation of the transformer, the vibration caused by the magnetostriction of the iron core silicon steel sheet and the electrodynamic force of the windings will radiate vibro-acoustic signals of different amplitudes and frequencies to the surroundings. When the transformer is in normal operation, it emits uniform low-frequency noise; if it emits uneven sound, it is an abnormal phenomenon. The transformer will emit distinct sounds under different operating states, and the operating status of the transformer can be grasped by detecting the sound it emits. It is worth noting that the detection of the sound of the transformer under different operating states can not only detect many serious faults that cause changes in electrical quantities, but also detect many abnormal states that do not endanger the insulation and do not cause changes in electrical quantities, such as inside and outside the transformer. loose parts, etc.

Since the vibration and sound detection method utilizes the vibration signal sent by the transformer, it is easily affected by the working environment, resulting in interruption of signal transmission and serious degradation of signal quality, making part of the received vibration and sound signal unusable, so how to effectively reconstruct Transformer vibration and sound signal is an important restricting factor for the successful application of this method. The methods commonly used now do not pay enough attention to this problem, and no effective measures have been taken to solve this problem.

SUMMARY OF THE INVENTION

The purpose of the present invention is to provide a method and system for reconstructing the vibro-acoustic signal of a transformer using the Hays matrix. The proposed method utilizes the continuity of the vibrating and acoustic signal of the transformer to reconstruct the signal according to the Hays matrix. The proposed method has good robustness and simple calculation.

For achieving the above object, the present invention provides the following scheme:

A method for reconstructing a transformer vibration-acoustic signal using a Hays matrix, comprising:

Step 1, input the measured transformer vibration and sound signal sequence S;

Step 2, according to the Hays matrix, reconstruct the transformer vibration-acoustic signal sequence S, and the reconstructed signal sequence is S _NEW ; specifically,

where D is the sea

W _OPT is the optimal weight matrix of Hayes; λ _OPT is the optimal adjustment of Hayes

matrix; α is the Hayes adjustment factor; μ is the Hays factor.

A transformer vibro-acoustic signal reconstruction system using Hayes matrix, comprising:

The acquisition module, input the measured transformer vibration and sound signal sequence S;

The reconstruction module reconstructs the transformer vibration-acoustic signal sequence S according to the Hays matrix, and reconstructs the

The latter signal sequence is S _NEW ; specifically,

其中，D为海斯预测矩阵；W_OPT为海斯最佳权重矩阵；λ_OPT为海斯最佳调整矩阵；α为海斯调整因子；μ为海斯因子。

Among them, D is the Hays prediction matrix; W _OPT is the Hays optimal weight matrix; λ _OPT is the Hays optimal adjustment matrix; α is the Hays adjustment factor; μ is the Hays factor.

According to the specific embodiments provided by the present invention, the present invention discloses the following technical effects:

Although the transformer vibration and acoustic signal detection technology has a wide range of applications and the technology is relatively mature, because the vibration and acoustic detection method utilizes the vibration signal sent by the transformer, it is easily affected by the working environment, resulting in interruption of signal transmission and serious degradation of signal quality. , making the received part of the vibro-acoustic signal unusable, so how to effectively reconstruct the transformer's vibrating-acoustic signal is an important restrictive factor for the successful application of this method. The methods commonly used now do not pay enough attention to this problem, and no effective measures have been taken to solve this problem.

The purpose of the present invention is to provide a method and system for reconstructing the vibro-acoustic signal of a transformer using the Hays matrix. The proposed method utilizes the continuity of the vibrating and acoustic signal of the transformer to reconstruct the signal according to the Hays matrix. The proposed method has good robustness and simple calculation.

Description of drawings

In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the accompanying drawings required in the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention, and for those of ordinary skill in the art, other drawings can also be obtained from these drawings without creative effort.

Fig. 1 is the method flow schematic diagram of the present invention;

Fig. 2 is the system structure schematic diagram of the present invention;

FIG. 3 is a schematic flowchart of a specific implementation case of the present invention.

Detailed ways

The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some, but not all, embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

In order to make the above objects, features and advantages of the present invention more clearly understood, the present invention will be described in further detail below with reference to the accompanying drawings and specific embodiments.

Fig. 1 is a schematic flow chart of a method for reconstructing vibrating-acoustic signals of transformers using Hays matrix

FIG. 1 is a schematic flowchart of a method for reconstructing a transformer vibration-acoustic signal using a Hays matrix according to the present invention. As shown in Fig. 1, the described method for reconstructing the vibration-acoustic signal of a transformer using a Hays matrix specifically includes the following steps:

Step 1, input the measured transformer vibration and sound signal sequence S;

Step 2, according to the Hays matrix, reconstruct the transformer vibration-acoustic signal sequence S, and the reconstructed signal sequence is S _NEW ; specifically,

其中，D为海斯预测矩阵；W_OPT为海斯最佳权重矩阵；λ_OPT为海斯最佳调整矩阵；α为海斯调整因子；μ为海斯因子。

Among them, D is the Hays prediction matrix; W _OPT is the Hays optimal weight matrix; λ _OPT is the Hays optimal adjustment matrix; α is the Hays adjustment factor; μ is the Hays factor.

Before the step 2, the method further includes:

Step 3: Obtain the Hays prediction matrix D, the Hays optimal weight matrix W _OPT , the Hays optimal adjustment matrix λ _OPT , the Hays adjustment factor α and the Hays factor μ.

The step 3 includes:

Step 301, obtain the cyclic delay matrix D _C , specifically:

in:

s _n : the n-th element of the signal sequence S [n=1,2,...,N]

N: the length of the signal sequence S

Step 302, obtaining the Hays prediction matrix D, specifically:

D=[S ^T SI][ΣV+I]D _C

in:

Σ: matrix of eigenvalues of matrix [D _C +I] ^-1

V: the right eigenvector matrix of the matrix [D _C +I] ^-1

I: identity matrix

Step 303: Obtain the Hays adjustment factor α, specifically:

in:

矩阵[S^TS][D+I]^-1的最大特征值

The largest eigenvalue of the matrix [S ^T S][D+I] ^-1

矩阵[S^TS][D+I]^-1的最小特征值

Minimum eigenvalue of matrix [S ^T S][D+I] ^-1

Step 304, obtaining the Hays factor μ, specifically:

in:

L _MAX : the maximum value of the absolute value of all elements in the matrix [S ^T S][D+I] ^-1

L _MIN : the minimum value of the absolute value of all elements in the matrix [S ^T S][D+I] ^-1

Step 305, iteratively obtain the optimal Hayes weight matrix W _OPT and the optimal Hayes adjustment matrix λ _OPT , specifically:

The first step: iterative initialization, specifically:

λ ₁ =[Sm _S ] ^T [Sm _S ]: the initialization value of the Hayes optimal adjustment matrix

W ₁ =ΨV: Initialization value of the Hays optimal weight matrix

k=1: iterative control parameter

in

所述W₁的特征值矩阵[i＝1,2,…,N_Ψ]

The eigenvalue matrix of the W ₁ [i=1,2,...,N _Ψ ]

The ith eigenvalue in the eigenvalue matrix of the matrix W ₁

所述循环延迟矩阵D_C的第i_L个特征值

The ith _L eigenvalue of the cyclic delay matrix D _C

N _Ψ : the number of non-zero eigenvalues in the eigenvalue matrix Ψ of the matrix W ₁

m _S : the mean value of the signal sequence S

The second step: iterative update, specifically:

in:

d: the first intermediate parameter used to find the minimum value

c: The second intermediate parameter used to find the minimum value

v: the third intermediate parameter used to find the minimum value

U _O : left eigenvector matrix of matrix [S ^T SI] ^-1

Σ _O : matrix of eigenvalues of matrix [S ^T SI] ^-1

The third step: the iteration is terminated, specifically, the iterative control parameter k is increased by 1, and the second step is repeated until the difference between the adjacent two iteration results is less than 0.001, at which time k=K, W _OPT =W _K+1 and λ _OPT =λ _K+1 .

Fig. 2 The structural intention of a transformer vibro-acoustic signal reconstruction system using Hays matrix

FIG. 2 is a schematic structural diagram of a system for reconstructing a transformer vibration-acoustic signal using a Hays matrix according to the present invention. As shown in Figure 2, the described system for reconstructing the vibrating and acoustic signal of a transformer using a Hays matrix includes the following structures:

Obtaining module 401, inputting the measured transformer vibration-acoustic signal sequence S;

The reconstruction module 402 reconstructs the transformer vibration-acoustic signal sequence S according to the Hays matrix,

The reconstructed signal sequence is S _NEW ; specifically,

其中，D为海斯预测矩阵；W_OPT为海斯最佳权重矩阵；λ_OPT为海斯最佳调整矩阵；α为海斯调整因子；μ为海斯因子。

Among them, D is the Hays prediction matrix; W _OPT is the Hays optimal weight matrix; λ _OPT is the Hays optimal adjustment matrix; α is the Hays adjustment factor; μ is the Hays factor.

The system also includes:

The calculation module 403 obtains the Hays prediction matrix D, the Hays optimal weight matrix W _OPT , the Hays optimal adjustment matrix λ _OPT , the Hays adjustment factor α and the Hays factor μ.

The computing module 403 also includes the following units, specifically:

The first calculation unit 4031 obtains the cyclic delay matrix D _C , specifically:

in:

s _n : the n-th element of the signal sequence S [n=1,2,...,N]

N: the length of the signal sequence S

The second computing unit 4032 obtains the Hays prediction matrix D, specifically:

D=[S ^T SI][ΣV+I]D _C

in:

Σ: matrix of eigenvalues of matrix [D _C +I] ^-1

V: the right eigenvector matrix of the matrix [D _C +I] ^-1

I: identity matrix

The third computing unit 4033 obtains the Hays adjustment factor α, specifically:

in:

矩阵[S^TS][D+I]^-1的最大特征值

The largest eigenvalue of the matrix [S ^T S][D+I] ^-1

矩阵[S^TS][D+I]^-1的最小特征值

Minimum eigenvalue of matrix [S ^T S][D+I] ^-1

The fourth calculation unit 4034 obtains the Hays factor μ, specifically:

in:

L _MAX : the maximum value of the absolute value of all elements in the matrix [S ^T S][D+I] ^-1

L _MIN : the minimum value of the absolute value of all elements in the matrix [S ^T S][D+I] ^-1

The iterative unit 4035, iteratively obtains the optimal Hayes weight matrix W _OPT and the optimal Hayes adjustment matrix λ _OPT , specifically:

The first step: iterative initialization, specifically:

λ ₁ =[Sm _S ] ^T [Sm _S ]: the initialization value of the Hayes optimal adjustment matrix

W ₁ =ΨV: Initialization value of the Hays optimal weight matrix

k=1: iterative control parameter

in

The eigenvalue matrix of the W ₁ [i=1,2,...,N _Ψ ]

The ith eigenvalue in the eigenvalue matrix of the matrix W ₁

所述循环延迟矩阵D_C的第i_L个特征值

The ith _L eigenvalue of the cyclic delay matrix D _C

N _Ψ : the number of non-zero eigenvalues in the eigenvalue matrix Ψ of the matrix W ₁

m _S : the mean value of the signal sequence S

The second step: iterative update, specifically:

in:

d: the first intermediate parameter used to find the minimum value

c: The second intermediate parameter used to find the minimum value

v: the third intermediate parameter used to find the minimum value

U _O : left eigenvector matrix of matrix [S ^T SI] ^-1

Σ _O : matrix of eigenvalues of matrix [S ^T SI] ^-1

The third step: the iteration is terminated, specifically, the iterative control parameter k is increased by 1, and the second step is repeated until the difference between the adjacent two iteration results is less than 0.001, at which time k=K, W _OPT =W _K+1 and λ _OPT =λ _K+1 .

A specific implementation case is provided below to further illustrate the solution of the present invention

FIG. 3 is a schematic flowchart of a specific implementation case of the present invention. As shown in Figure 3, it specifically includes the following steps:

1. Input the measured PLC signal sequence

S=[s ₁ ,s ₂ ,...,s _N-1 ,s _N ]

in:

S: The measured PLC signal data sequence, the length is N

s _i , i=1,2,...,N: the measured PLC signal with serial number i

2. Find the circular delay matrix

in:

s _n : the n-th element of the signal sequence S [n=1,2,...,N]

N: the length of the signal sequence S

3. Find the Hays prediction matrix

D=[S ^T SI][ΣV+I]D _C

in:

Σ: matrix of eigenvalues of matrix [D _C +I] ^-1

V: the right eigenvector matrix of the matrix [D _C +I] ^-1

I: identity matrix

4. Find the Hayes adjustment factor

in:

矩阵[S^TS][D+I]^-1的最大特征值

The largest eigenvalue of the matrix [S ^T S][D+I] ^-1

矩阵[S^TS][D+I]^-1的最小特征值

Minimum eigenvalue of matrix [S ^T S][D+I] ^-1

5. Find the Hayes factor

in:

L _MAX : the maximum value of the absolute value of all elements in the matrix [S ^T S][D+I] ^-1

L _MIN : the minimum value of the absolute value of all elements in the matrix [S ^T S][D+I] ^-1

6. Iteratively obtain the Hays optimal weight matrix and the Hays optimal adjustment matrix, specifically:

The first step: iterative initialization, specifically:

λ ₁ =[Sm _S ] ^T [Sm _S ]: the initialization value of the Hayes optimal adjustment matrix

W ₁ =ΨV: Initialization value of the Hays optimal weight matrix

k=1: iterative control parameter

in

所述W₁的特征值矩阵[i＝1,2,…,N_Ψ]

The eigenvalue matrix of the W ₁ [i=1,2,...,N _Ψ ]

The ith eigenvalue in the eigenvalue matrix of the matrix W ₁

所述循环延迟矩阵D_C的第i_L个特征值

The ith _L eigenvalue of the cyclic delay matrix D _C

N _Ψ : the number of non-zero eigenvalues in the eigenvalue matrix Ψ of the matrix W ₁

m _S : the mean value of the signal sequence S

The second step: iterative update, specifically:

in:

d: the first intermediate parameter used to find the minimum value

c: The second intermediate parameter used to find the minimum value

v: the third intermediate parameter used to find the minimum value

U _O : left eigenvector matrix of matrix [S ^T SI] ^-1

Σ _O : matrix of eigenvalues of matrix [S ^T SI] ^-1

The third step: the iteration is terminated, specifically, the iterative control parameter k is increased by 1, and the second step is repeated until the difference between the adjacent two iteration results is less than 0.001, at which time k=K, W _OPT =W _K+1 and λ _OPT =λ _K+1 .

7. Refactoring

According to the multi-optimization theory, the transformer vibration-acoustic signal sequence S is reconstructed, and the reconstructed signal sequence is S _NEW ; specifically,

其中，D为海斯预测矩阵；W_OPT为海斯最佳权重矩阵；λ_OPT为海斯最佳调整矩阵；α为海斯调整因子；μ为海斯因子。

Among them, D is the Hays prediction matrix; W _OPT is the Hays optimal weight matrix; λ _OPT is the Hays optimal adjustment matrix; α is the Hays adjustment factor; μ is the Hays factor.

The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments, and the same and similar parts between the various embodiments can be referred to each other. For the system disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant part can be referred to the description of the method.

The principles and implementations of the present invention are described herein using specific examples. The descriptions of the above embodiments are only used to help understand the method and the core idea of the present invention; meanwhile, for those skilled in the art, according to the present invention There will be changes in the specific implementation and application scope. In conclusion, the contents of this specification should not be construed as limiting the present invention.

Claims (5)

1. a method for reconstructing the oscillatory sound signal of a transformer utilizing a Hays matrix, is characterized in that, comprising: Step 1, input the measured transformer vibration and sound signal sequence S; Step 2, according to the Hays matrix, reconstruct the transformer vibration-acoustic signal sequence S, and the reconstructed signal sequence is S _NEW ; specifically, Among them, D is the Hays prediction matrix; W _OPT is the Hays optimal weight matrix; λ _OPT is the Hays optimal adjustment matrix; α is the Hays adjustment factor; μ is the Hays factor. 2. The method according to claim 1, wherein before the step 2, the method further comprises: Step 3: Obtain the Hays prediction matrix D, the Hays optimal weight matrix W _OPT , the Hays optimal adjustment matrix λ _OPT , the Hays adjustment factor α and the Hays factor μ. 3. The method according to claim 2, wherein the step 3 comprises: Step 301, obtain the cyclic delay matrix D _C , specifically: in: s _n : the n-th element of the signal sequence S [n=1,2,...,N] N: the length of the signal sequence S Step 302, obtaining the Hays prediction matrix D, specifically: D=[S ^T SI][ΣV+I]D _C in: Σ: matrix of eigenvalues of matrix [D _C +I] ^-1 V: the right eigenvector matrix of the matrix [D _C +I] ^-1 I: identity matrix Step 303: Obtain the Hays adjustment factor α, specifically:

in: The largest eigenvalue of the matrix [S ^T S][D+I] ^-1

Minimum eigenvalue of matrix [S ^T S][D+I] ^-1 Step 304, obtaining the Hays factor μ, specifically:

in: L _MAX : the maximum value of the absolute value of all elements in the matrix [S ^T S][D+I] ^-1 L _MIN : the minimum value of the absolute value of all elements in the matrix [S ^T S][D+I] ^-1 Step 305, iteratively obtain the optimal Hayes weight matrix W _OPT and the optimal Hayes adjustment matrix λ _OPT , specifically: The first step: iterative initialization, specifically: λ ₁ =[Sm _S ] ^T [Sm _S ]: the initialization value of the Hayes optimal adjustment matrix W ₁ =ΨV: Initialization value of the Hays optimal weight matrix k=1: iterative control parameter in

The eigenvalue matrix of the W ₁ [i=1,2,...,N _Ψ ]

The ith eigenvalue in the eigenvalue matrix of the matrix W ₁ ζ _iL : the ith _L eigenvalue of the cyclic delay matrix D _C N _Ψ : the number of non-zero eigenvalues in the eigenvalue matrix Ψ of the matrix W ₁ m _S : the mean value of the signal sequence S The second step: iterative update, specifically:

in: d: the first intermediate parameter used to find the minimum value c: The second intermediate parameter used to find the minimum value v: the third intermediate parameter used to find the minimum value U _O : left eigenvector matrix of matrix [S ^T SI] ^-1 Σ _O : matrix of eigenvalues of matrix [S ^T SI] ^-1 The third step: the iteration is terminated, specifically, the iterative control parameter k is increased by 1, and the second step is repeated until the difference between the adjacent two iteration results is less than 0.001, at which time k=K, W _OPT =W _K+1 and λ _OPT =λ _K+1 . 4. a transformer vibrating-acoustic signal reconstruction system utilizing Hays matrix, is characterized in that, comprises: The acquisition module, input the measured transformer vibration and sound signal sequence S; The filtering module reconstructs the transformer vibration-acoustic signal sequence S according to the Hays matrix, and the reconstructed signal sequence is S _NEW ; specifically,

Among them, D is the Hays prediction matrix; W _OPT is the Hays optimal weight matrix; λ _OPT is the Hays optimal adjustment matrix; α is the Hays adjustment factor; μ is the Hays factor. 5. The system of claim 4, further comprising: The computing module obtains the Hays prediction matrix D, the Hays optimal weight matrix W _OPT , the Hays optimal adjustment matrix λ _OPT , the Hays adjustment factor α and the Hays factor μ.

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