CN112307999B - Transformer running state vibration and noise detection method and system based on ant colony optimization - Google Patents
Transformer running state vibration and noise detection method and system based on ant colony optimization Download PDFInfo
- Publication number
- CN112307999B CN112307999B CN202011229107.5A CN202011229107A CN112307999B CN 112307999 B CN112307999 B CN 112307999B CN 202011229107 A CN202011229107 A CN 202011229107A CN 112307999 B CN112307999 B CN 112307999B
- Authority
- CN
- China
- Prior art keywords
- matrix
- row
- cyclic delay
- column
- window
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Expired - Fee Related
Links
- 238000005457 optimization Methods 0.000 title claims abstract description 34
- 238000001514 detection method Methods 0.000 title claims abstract description 30
- 239000011159 matrix material Substances 0.000 claims abstract description 147
- 125000004122 cyclic group Chemical group 0.000 claims abstract description 103
- 238000000034 method Methods 0.000 claims abstract description 38
- 108010076504 Protein Sorting Signals Proteins 0.000 claims abstract description 36
- 238000001914 filtration Methods 0.000 claims abstract description 19
- 230000002159 abnormal effect Effects 0.000 claims description 10
- 238000001704 evaporation Methods 0.000 claims description 7
- 230000008020 evaporation Effects 0.000 claims description 7
- 239000003016 pheromone Substances 0.000 claims description 7
- 238000005070 sampling Methods 0.000 claims description 7
- 238000005516 engineering process Methods 0.000 description 3
- 230000007613 environmental effect Effects 0.000 description 3
- 238000012544 monitoring process Methods 0.000 description 3
- 238000010586 diagram Methods 0.000 description 2
- 230000000694 effects Effects 0.000 description 1
- 238000009413 insulation Methods 0.000 description 1
- 230000000750 progressive effect Effects 0.000 description 1
- 238000004804 winding Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2218/00—Aspects of pattern recognition specially adapted for signal processing
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/16—Matrix or vector computation, e.g. matrix-matrix or matrix-vector multiplication, matrix factorization
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/004—Artificial life, i.e. computing arrangements simulating life
- G06N3/006—Artificial life, i.e. computing arrangements simulating life based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2218/00—Aspects of pattern recognition specially adapted for signal processing
- G06F2218/02—Preprocessing
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Mathematical Physics (AREA)
- General Engineering & Computer Science (AREA)
- Data Mining & Analysis (AREA)
- Computing Systems (AREA)
- Pure & Applied Mathematics (AREA)
- Mathematical Optimization (AREA)
- Mathematical Analysis (AREA)
- Computational Mathematics (AREA)
- Software Systems (AREA)
- Artificial Intelligence (AREA)
- Biomedical Technology (AREA)
- General Health & Medical Sciences (AREA)
- Molecular Biology (AREA)
- Evolutionary Computation (AREA)
- Computational Linguistics (AREA)
- Biophysics (AREA)
- Life Sciences & Earth Sciences (AREA)
- Health & Medical Sciences (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Signal Processing (AREA)
- Algebra (AREA)
- Databases & Information Systems (AREA)
- Magnetic Resonance Imaging Apparatus (AREA)
Abstract
The embodiment of the invention discloses a transformer running state vibration and noise detection method and system by utilizing ant colony optimization, wherein the method comprises the following steps: step 101, acquiring a signal sequence S acquired according to a time sequence; step 102, generating a cyclic delay signal matrix; 103, generating N window cyclic delay sub-matrixes; step 104 initializing process parameters; step 105, updating process parameters; step 106, calculating an updating difference value and finishing updating; step 107, filtering the N window delay circulation sub-matrixes; step 108, solving ant colony optimization values of N windows; step 109 judges the running state of the transformer.
Description
Technical Field
The invention relates to the field of electric power, in particular to a method and a system for detecting vibration and sound of a transformer in an operation state.
Background
With the high-speed development of the smart grid, the safe and stable operation of the power equipment is particularly important. At present, the detection of the operating state of the power equipment with ultrahigh voltage and above voltage grades, especially the detection of the abnormal state, is increasingly important and urgent. As an important component of an electric power system, a power transformer is one of the most important electrical devices in a substation, and its reliable operation is related to the safety of a power grid.
The basic principle of the transformer operation state detection is to extract each characteristic quantity in the transformer operation, analyze, identify and track the characteristic quantity so as to monitor the abnormal operation state of the transformer. The current common detection methods for the operation state of the transformer include a pulse current method and an ultrasonic detection method for detecting partial discharge, a frequency response method for detecting winding deformation, a vibration detection method for detecting mechanical and electrical faults, and the like. The detection methods mainly detect the insulation condition and the mechanical structure condition of the transformer, wherein the detection of the vibration signal (vibration sound) of the transformer is the most comprehensive, and the fault and the abnormal state of most transformers can be reflected.
Although the transformer vibration and sound detection method is widely applied to monitoring the running state of the transformer and the technology is relatively mature, the vibration and sound detection method utilizes the vibration signal sent by the transformer and is easily influenced by the environmental noise, so that the method often cannot obtain satisfactory results when being applied in the actual working environment.
Disclosure of Invention
As mentioned above, the transformer vibration and noise detection method is widely applied to monitoring the operation state of the transformer, and the technology is relatively mature, but because the vibration and noise detection method utilizes the vibration signal emitted by the transformer, the vibration and noise detection method is easily affected by the environmental noise, and therefore, the method often fails to obtain satisfactory results when being applied in the actual working environment.
The invention aims to provide a transformer running state vibration sound detection method and system based on ant colony optimization. The method has better robustness and simpler calculation.
In order to achieve the purpose, the invention provides the following scheme:
a transformer running state vibration and noise detection method using ant colony optimization comprises the following steps:
step 101, acquiring a signal sequence S acquired according to a time sequence;
step 102, generating a cyclic delay signal matrix, specifically: the cyclic delay signal matrix is denoted as D, and the ith row and jth column elements thereof are denoted as DijThe formula used is:
wherein:
|i-1+j|Nmeaning that the remainder is taken modulo N for i-1+ j,
i is 1,2, N is a row number,
j is 1,2, N is a column number,
n is the length of the signal sequence S;
step 103, generating N window cyclic delay sub-matrices, specifically: the K window cyclic delay sub-matrix is marked as JKThe formula used is:
wherein:
dK-1,K-1is the K-1 column element of the K-1 row of the cyclic delay signal matrix D,
dK,K-1is the K-1 column element of the K-th row of the cyclic delay signal matrix D,
dK+1,K-1: the K +1 row, the K-1 column element of the cyclic delay signal matrix D
dK-1,K+1Is the K-1 row and the K +1 column elements of the cyclic delay signal matrix D,
dK,K+1is the K +1 th row element of the cyclic delay signal matrix D,
dK+1,K+1is the element of the K +1 th row and the K +1 th column of the cyclic delay signal matrix D,
dK-1,Kis the K-1 row and the K column element of the cyclic delay signal matrix D,
dK+1,Kis the K +1 row and K column elements of the cyclic delay signal matrix D,
dK,Kis the Kth row and the Kth column element of the cyclic delay signal matrix D,
if K-1<1, K-1 is set to K,
if K +1 > N, K +1 is set to N,
k is 1,2, N is a window serial number;
step 104 initializes process parameters, specifically: the process parameters comprise an influence factor alpha, an evaporation rate rho, a constant factor C, an iteration control parameter k and a connection matrix gamma; the initialized value of the connection matrix gamma is recorded as gamma0(ii) a The initialization formula used is:
α=0.41
ρ=0.6
C=0.0001
k=0
γ0=03×3;
step 105 updates process parameters, specifically: the updated value of the k +1 th step of the connection matrix gamma is recorded as gammak+1The element in the first row and the m column is marked asThe update formula used is:
wherein:
t is the sampling interval of the signal sequence S,
updating the k-th step of the connection matrix gamma with the new value gammakThe row l and the column m elements,
l is 1,2,3 is a connection row number,
m is 1,2 and 3 are connecting column serial numbers;
step 106, obtaining an update difference value and ending the update, specifically: the updated difference is recorded as epsilon, and the solving formula is as follows:
ε=|γk+1-γk|
if the updating difference value epsilon meets the formula epsilon is more than or equal to 0.001, adding 1 to the value of the iteration control parameter k, and returning to the step 105 and the step 106 for updating again; otherwise, iteration is finished and a filter factor omega is obtainedlmHas a value of
Step 107, filtering the N window delay circulation sub-matrices, specifically: filtering the K window cyclic delay sub-matrixThe latter result is denoted yKThe filtering formula is as follows:
wherein:
step 108, solving ant colony optimization values of N windows, specifically: the K window ant colony optimization value is recorded as HKThe formula used is:
HK=||YG||F
wherein:
||YG||Frepresenting the Frobenius norm of the matrix YG,
σ is the mean square error of the signal sequence S;
step 109, judging the running state of the transformer, specifically: if the K window ant colony optimization value HKIs greater than or equal toThe transformer is in an abnormal operation state at the Kth point; otherwise, the transformer is in a normal operation state.
An ant colony optimized transformer operating condition vibro-acoustic detection system, comprising:
the module 201 acquires a signal sequence S acquired in time sequence;
module 202 generates a matrix of cyclic delay signalsThe body is as follows: the cyclic delay signal matrix is denoted as D, and the ith row and jth column elements thereof are denoted as DijThe formula used is:
wherein:
|i-1+j|Nmeaning that the remainder is taken modulo N for i-1+ j,
i is 1,2, N is a row number,
j is 1,2, N is a column number,
n is the length of the signal sequence S;
the module 203 generates N window cyclic delay sub-matrices, specifically: the K window cyclic delay sub-matrix is marked as JKThe formula used is:
wherein:
dK-1,K-1is the K-1 column element of the K-1 row of the cyclic delay signal matrix D,
dK,K-1is the K-1 column element of the K-th row of the cyclic delay signal matrix D,
dK+1,K-1: the K +1 row, the K-1 column element of the cyclic delay signal matrix D
dK-1,K+1Is the K-1 row and the K +1 column elements of the cyclic delay signal matrix D,
dK,K+1is the K +1 th row element of the cyclic delay signal matrix D,
dK+1,K+1is the element of the K +1 th row and the K +1 th column of the cyclic delay signal matrix D,
dK-1,Kis that it isThe K-1 row and K column elements of the cyclic delay signal matrix D,
dK+1,Kis the K +1 row and K column elements of the cyclic delay signal matrix D,
dK,Kis the Kth row and the Kth column element of the cyclic delay signal matrix D,
if K-1<1, K-1 is set to K,
if K +1 > N, K +1 is set to N,
k is 1,2, N is a window serial number;
the module 204 initializes process parameters, specifically: the process parameters comprise an influence factor alpha, an evaporation rate rho, a constant factor C, an iteration control parameter k and a connection matrix gamma; the initialized value of the connection matrix gamma is recorded as gamma0(ii) a The initialization formula used is:
α=0.41
ρ=0.6
C=0.0001
k=0
γ0=03×3;
the module 205 updates the process parameters, specifically: the updated value of the k +1 th step of the connection matrix gamma is recorded as gammak+1The element in the first row and the m column is marked asThe update formula used is:
wherein:
t is the sampling interval of the signal sequence S,
updating the k-th step of the connection matrix gamma with the new value gammakThe row l and the column m elements,
l is 1,2,3 is a connection row number,
m is 1,2 and 3 are connecting column serial numbers;
the module 206 calculates an update difference and finishes updating, specifically: the updated difference is recorded as epsilon, and the solving formula is as follows:
ε=|γk+1-γk|
if the update difference epsilon satisfies the formula epsilon is more than or equal to 0.001, adding 1 to the value of the iteration control parameter k, and returning to the module 205 and the module 206 for updating again; otherwise, iteration is finished and a filter factor omega is obtainedlmHas a value of
The module 207 filters the N window delay cyclic sub-matrices, specifically: filtering the K window cyclic delay sub-matrix, and recording the filtered result as yKThe filtering formula is as follows:
wherein:
the module 208 calculates ant colony optimization values for N windows, specifically: the K window ant colony optimization value is recorded as HKThe formula used is:
HK=||YG||F
wherein:
||YG||Frepresenting the Frobenius norm of the matrix YG,
σ is the mean square error of the signal sequence S;
the module 209 determines the running state of the transformer, specifically: if the K window ant colony optimization value HKIs greater than or equal toThe transformer is in an abnormal operation state at the Kth point; otherwise, the transformer is in a normal operation state.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects:
as mentioned above, the transformer vibration and noise detection method is widely applied to monitoring the operation state of the transformer, and the technology is relatively mature, but because the vibration and noise detection method utilizes the vibration signal emitted by the transformer, the vibration and noise detection method is easily affected by the environmental noise, and therefore, the method often fails to obtain satisfactory results when being applied in the actual working environment.
The invention aims to provide a transformer running state vibration sound detection method and system based on ant colony optimization. The method has better robustness and simpler calculation.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the embodiments will be briefly described below. It is obvious that the drawings in the following description are only some embodiments of the invention, and that for a person skilled in the art, other drawings can be derived from them without inventive effort.
FIG. 1 is a schematic flow diagram of the process of the present invention;
FIG. 2 is a schematic flow chart of the system of the present invention;
FIG. 3 is a flow chart illustrating an embodiment of the present invention.
Detailed Description
The technical solution 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. It is to be understood that the described embodiments are merely exemplary of the invention, and not restrictive of the full scope of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in further detail below.
FIG. 1 is a schematic flow chart of a transformer operation state vibration and sound detection method using ant colony optimization
Fig. 1 is a schematic flow chart of a transformer operation state vibration and noise detection method using ant colony optimization according to the present invention. As shown in fig. 1, the method for detecting the vibration and noise of the transformer operating state by ant colony optimization specifically includes the following steps:
step 101, acquiring a signal sequence S acquired according to a time sequence;
step 102, generating a cyclic delay signal matrix, specifically: the cyclic delay signal matrix is denoted as D, and the ith row and jth column elements thereof are denoted as DijThe formula used is:
wherein:
|i-1+j|Nmeaning that the remainder is taken modulo N for i-1+ j,
i is 1,2, N is a row number,
j is 1,2, N is a column number,
n is the length of the signal sequence S;
step 103, generating N window cyclic delay sub-matrices, specifically: the K window cyclic delay sub-matrix is marked as JKThe formula used is:
wherein:
dK-1,K-1is the K-1 column element of the K-1 row of the cyclic delay signal matrix D,
dK,K-1is the K-1 column element of the K-th row of the cyclic delay signal matrix D,
dK+1,K-1: the K +1 row, the K-1 column element of the cyclic delay signal matrix D
dK-1,K+1Is the K-1 row and the K +1 column elements of the cyclic delay signal matrix D,
dK,K+1is the K +1 th row element of the cyclic delay signal matrix D,
dK+1,K+1is the element of the K +1 th row and the K +1 th column of the cyclic delay signal matrix D,
dK-1,Kis the K-1 row and the K column element of the cyclic delay signal matrix D,
dK+1,Kis the K +1 row and K column elements of the cyclic delay signal matrix D,
dK,Kis the Kth row and the Kth column element of the cyclic delay signal matrix D,
if K-1<1, K-1 is set to K,
if K +1 > N, K +1 is set to N,
k is 1,2, N is a window serial number;
step 104 initializes process parameters, specifically: the process parameters comprise an influence factor alpha, an evaporation rate rho, a constant factor C, an iteration control parameter k and a connection matrix gamma; the initialized value of the connection matrix gamma is recorded as gamma0(ii) a The initialization formula used is:
α=0.41
ρ=0.6
C=0.0001
k=0
γ0=03×3;
step 105 updates process parameters, specifically: the updated value of the k +1 th step of the connection matrix gamma is recorded as gammak+1The element in the first row and the m column is marked asThe update formula used is:
wherein:
t is the sampling interval of the signal sequence S,
updating the k-th step of the connection matrix gamma with the new value gammakThe row l and the column m elements,
l is 1,2,3 is a connection row number,
m is 1,2 and 3 are connecting column serial numbers;
step 106, obtaining an update difference value and ending the update, specifically: the updated difference is recorded as epsilon, and the solving formula is as follows:
ε=|γk+1-γk|
if the updating difference value epsilon meets the formula epsilon is more than or equal to 0.001, adding 1 to the value of the iteration control parameter k, and returning to the step 105 and the step 106 for updating again; otherwise, iteration is finished and a filter factor omega is obtainedlmHas a value of
Step 107, filtering the N window delay circulation sub-matrices, specifically: filtering the K window cyclic delay sub-matrix, and recording the filtered result as yKThe filtering formula is as follows:
wherein:
step 108, solving ant colony optimization values of N windows, specifically: the K window ant colony optimization value is recorded as HKThe formula used is:
HK=||YG||F
wherein:
||YG||Frepresenting the Frobenius norm of the matrix YG,
σ is the mean square error of the signal sequence S;
step 109, judging the running state of the transformer, specifically: if the K window ant colony optimization value HKIs greater than or equal toThe transformer is in an abnormal operation state at the Kth point; otherwise, the transformer is in a normal operation state.
FIG. 2 structural intent of transformer operating state vibro-acoustic detection system using ant colony optimization
Fig. 2 is a schematic structural diagram of a transformer operating state vibration and noise detection system using ant colony optimization according to the present invention. As shown in fig. 2, the transformer operating state vibration and noise detection system using ant colony optimization includes the following structures:
the module 201 acquires a signal sequence S acquired in time sequence;
the module 202 generates a cyclic delay signal matrix, specifically: the cyclic delay signal matrix is denoted as D, and the ith row and jth column elements thereof are denoted as DijThe formula used is:
wherein:
|i-1+j|Nmeaning that the remainder is taken modulo N for i-1+ j,
i is 1,2, N is a row number,
j is 1,2, N is a column number,
n is the length of the signal sequence S;
the module 203 generates N window cyclic delay sub-matrices, specifically: the K window cyclic delay sub-matrix is marked as JKThe formula used is:
wherein:
dK-1,K-1is the K-1 column element of the K-1 row of the cyclic delay signal matrix D,
dK,K-1is the K-1 column element of the K-th row of the cyclic delay signal matrix D,
dK+1,K-1: the K +1 row, the K-1 column element of the cyclic delay signal matrix D
dK-1,K+1Is the K-1 row and the K +1 column elements of the cyclic delay signal matrix D,
dK,K+1is the K +1 th row element of the cyclic delay signal matrix D,
dK+1,K+1is the element of the K +1 th row and the K +1 th column of the cyclic delay signal matrix D,
dK-1,Kis the K-1 row and the K column element of the cyclic delay signal matrix D,
dK+1,Kis the K +1 row and K column elements of the cyclic delay signal matrix D,
dK,Kis the Kth row and the Kth column element of the cyclic delay signal matrix D,
if K-1<1, K-1 is set to K,
if K +1 > N, K +1 is set to N,
k is 1,2, N is a window serial number;
the module 204 initializes process parameters, specifically: the process parameters comprise an influence factor alpha, an evaporation rate rho, a constant factor C, an iteration control parameter k and a connection matrix gamma; the initialized value of the connection matrix gamma is recorded as gamma0(ii) a The initialization formula used is:
α=0.41
ρ=0.6
C=0.0001
k=0
γ0=03×3;
the module 205 updates the process parameters, specifically: the updated value of the k +1 th step of the connection matrix gamma is recorded as gammak+1The element in the first row and the m column is marked asThe update formula used is:
wherein:
t is the sampling interval of the signal sequence S,
updating the k-th step of the connection matrix gamma with the new value gammakThe row l and the column m elements,
l is 1,2,3 is a connection row number,
m is 1,2 and 3 are connecting column serial numbers;
the module 206 calculates an update difference and finishes updating, specifically: the updated difference is recorded as epsilon, and the solving formula is as follows:
ε=|γk+1-γk|
if the update difference epsilon satisfies the formula epsilon is more than or equal to 0.001, adding 1 to the value of the iteration control parameter k, and returning to the module 205 and the module 206 for updating again; otherwise, iteration is finished and a filter factor omega is obtainedlmHas a value of
The module 207 filters the N window delay cyclic sub-matrices, specifically: filtering the K window cyclic delay sub-matrix, and recording the filtered result as yKFiltering ofThe formula is as follows:
wherein:
the module 208 calculates ant colony optimization values for N windows, specifically: the K window ant colony optimization value is recorded as HKThe formula used is:
HK=||YG||F
wherein:
||YG||Frepresenting the Frobenius norm of the matrix YG,
σ is the mean square error of the signal sequence S;
the module 209 determines the running state of the transformer, specifically: if the K window ant colony optimization value HKIs greater than or equal toThe transformer is in an abnormal operation state at the Kth point; otherwise, the transformer is in a normal operation state.
The following provides an embodiment for further illustrating the invention
FIG. 3 is a flow chart illustrating an embodiment of the present invention. As shown in fig. 3, the method specifically includes the following steps:
step 301, acquiring a signal sequence S acquired according to a time sequence;
step 302 generates a cyclic delay signal matrix, specifically: the cyclic delay signal matrix is denoted as D, and the ith row and jth column elements thereof are denoted as DijThe formula used is:
wherein:
|i-1+j|Nmeaning that the remainder is taken modulo N for i-1+ j,
i is 1,2, N is a row number,
j is 1,2, N is a column number,
n is the length of the signal sequence S;
step 303 generates N window cyclic delay sub-matrices, specifically: the K window cyclic delay sub-matrix is marked as JKThe formula used is:
wherein:
dK-1,K-1is the K-1 column element of the K-1 row of the cyclic delay signal matrix D,
dK,K-1is the K-1 column element of the K-th row of the cyclic delay signal matrix D,
dK+1,K-1: the K +1 row, the K-1 column element of the cyclic delay signal matrix D
dK-1,K+1Is the K-1 row and the K +1 column elements of the cyclic delay signal matrix D,
dK,K+1is the K +1 th row element of the cyclic delay signal matrix D,
dK+1,K+1for the K +1 th row of the cyclic delay signal matrix DThe elements of the K +1 th column,
dK-1,Kis the K-1 row and the K column element of the cyclic delay signal matrix D,
dK+1,Kis the K +1 row and K column elements of the cyclic delay signal matrix D,
dK,Kis the Kth row and the Kth column element of the cyclic delay signal matrix D,
if K-1<1, K-1 is set to K,
if K +1 > N, K +1 is set to N,
k is 1,2, N is a window serial number;
step 304 initializes process parameters, specifically: the process parameters comprise an influence factor alpha, an evaporation rate rho, a constant factor C, an iteration control parameter k and a connection matrix gamma; the initialized value of the connection matrix gamma is recorded as gamma0(ii) a The initialization formula used is:
α=0.41
ρ=0.6
C=0.0001
k=0
γ0=03×3;
step 305 updates process parameters, specifically: the updated value of the k +1 th step of the connection matrix gamma is recorded as gammak+1The element in the first row and the m column is marked asThe update formula used is:
wherein:
t is the sampling interval of the signal sequence S,
updating the k-th step of the connection matrix gamma with the new value gammakThe row l and the column m elements,
l is 1,2,3 is a connection row number,
m is 1,2 and 3 are connecting column serial numbers;
step 306, calculating an update difference and ending the update, specifically: the updated difference is recorded as epsilon, and the solving formula is as follows:
ε=|γk+1-γk|
if the updating difference value epsilon meets the formula epsilon is more than or equal to 0.001, adding 1 to the value of the iteration control parameter k, and returning to the step 305 and the step 306 for updating again; otherwise, iteration is finished and a filter factor omega is obtainedlmHas a value of
Step 307, filtering the N window delay circulation sub-matrices, specifically: filtering the K window cyclic delay sub-matrix, and recording the filtered result as yKThe filtering formula is as follows:
wherein:
step 308, obtaining ant colony optimization values of N windows, specifically: the K window ant colony optimization value is recorded as HKThe formula used is:
HK=||YG||F
wherein:
||YG||Frepresenting the Frobenius norm of the matrix YG,
σ is the mean square error of the signal sequence S;
step 309, determining the running state of the transformer, specifically: if the K window ant colony optimization value HKIs greater than or equal toThe transformer is in an abnormal operation state at the Kth point; otherwise, the transformer is in a normal operation state.
The embodiments in the present description are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other. For the system disclosed by the embodiment, the description is simple because the system corresponds to the method disclosed by the embodiment, and the relevant part can be referred to the method part for description.
The principles and embodiments of the present invention have been described herein using specific examples, which are provided only to help understand the method and the core concept of the present invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, the specific embodiments and the application range may be changed. In view of the above, the present disclosure should not be construed as limiting the invention.
Claims (2)
1. A transformer running state vibration and noise detection method using ant colony optimization is characterized by comprising the following steps:
step 101, acquiring a signal sequence S acquired according to a time sequence;
step 102, generating a cyclic delay signal matrix, specifically: the cyclic delay signal matrix is denoted as D, and the ith row and jth column elements thereof are denoted as DijThe formula used is:
wherein:
|i-1+j|Nmeaning that the remainder is taken modulo N for i-1+ j,
i is 1,2, N is a row number,
j is 1,2, N is a column number,
n is the length of the signal sequence S;
step 103, generating N window cyclic delay sub-matrices, specifically: the K window cyclic delay sub-matrix is marked as JKThe formula used is:
wherein:
dK-1,K-1is the K-1 column element of the K-1 row of the cyclic delay signal matrix D,
dK,K-1is the K-1 column element of the K-th row of the cyclic delay signal matrix D,
dK+1,K-1: the K +1 row, the K-1 column element of the cyclic delay signal matrix D
dK-1,K+1Is the K-1 row and the K +1 column elements of the cyclic delay signal matrix D,
dK,K+1is the K +1 th row element of the cyclic delay signal matrix D,
dK+1,K+1is the element of the K +1 th row and the K +1 th column of the cyclic delay signal matrix D,
dK-1,Kis the K-1 row and the K column element of the cyclic delay signal matrix D,
dK+1,Kis the K +1 row and K column elements of the cyclic delay signal matrix D,
dK,Kis the Kth row and the Kth column element of the cyclic delay signal matrix D,
if K-1<1, K-1 is set to K,
if K +1 > N, K +1 is set to N,
k is 1,2, N is a window serial number;
step 104 initializes process parameters, specifically: the process parameters comprise an influence factor alpha, an evaporation rate rho, a constant factor C, an iteration control parameter k and a connection matrix gamma; the initialized value of the connection matrix gamma is recorded as gamma0(ii) a The initialization formula used is:
α=0.41
ρ=0.6
C=0.0001
k=0
γ0=03×3;
step 105 updates process parameters, specifically: the updated value of the k +1 th step of the connection matrix gamma is recorded as gammak+1The element in the first row and the m column is marked asThe update formula used is:
wherein:
t is the sampling interval of the signal sequence S,
updating the k-th step of the connection matrix gamma with the new value gammakThe row l and the column m elements,
l is 1,2,3 is a connection row number,
m is 1,2 and 3 are connecting column serial numbers;
step 106, obtaining an update difference value and ending the update, specifically: the updated difference is recorded as epsilon, and the solving formula is as follows:
ε=|γk+1-γk|
if the updating difference value epsilon meets the formula epsilon is more than or equal to 0.001, adding 1 to the value of the iteration control parameter k, and returning to the step 105 and the step 106 for updating again; otherwise, iteration is finished and a filter factor omega is obtainedlmHas a value of
Step 107, filtering the N window delay circulation sub-matrices, specifically: filtering the K window cyclic delay sub-matrix, and recording the filtered result as yKThe filtering formula is as follows:
wherein:
step 108, solving ant colony optimization values of N windows, specifically: the K window ant colony optimization value is recorded as HKThe formula used is:
HK=||YG||F
wherein:
||YG||Frepresenting the Frobenius norm of the matrix YG,
σ is the mean square error of the signal sequence S;
2. An ant colony-optimized transformer operating state vibration and sound detection system, comprising:
the module 201 acquires a signal sequence S acquired in time sequence;
the module 202 generates a cyclic delay signal matrix, specifically: the cyclic delay signal matrix is denoted as D, and the ith row and jth column elements thereof are denoted as DijThe formula used is:
wherein:
|i-1+j|Nrepresenting modulo N to i-1+ j takes the remainder,
i is 1,2, N is a row number,
j is 1,2, N is a column number,
n is the length of the signal sequence S;
the module 203 generates N window cyclic delay sub-matrices, specifically: the K window cyclic delay sub-matrix is marked as JKThe formula used is:
wherein:
dK-1,K-1is the K-1 column element of the K-1 row of the cyclic delay signal matrix D,
dK,K-1is the K-1 column element of the K-th row of the cyclic delay signal matrix D,
dK+1,K-1: the K +1 row, the K-1 column element of the cyclic delay signal matrix D
dK-1,K+1Is the K-1 row and the K +1 column elements of the cyclic delay signal matrix D,
dK,K+1is the K +1 th row element of the cyclic delay signal matrix D,
dK+1,K+1is the element of the K +1 th row and the K +1 th column of the cyclic delay signal matrix D,
dK-1,Kis the K-1 row and the K column element of the cyclic delay signal matrix D,
dK+1,Kis the K +1 row and K column elements of the cyclic delay signal matrix D,
dK,Kis the Kth row and the Kth column element of the cyclic delay signal matrix D,
if K-1<1, K-1 is set to K,
if K +1 > N, K +1 is set to N,
k is 1,2, N is a window serial number;
the module 204 initializes process parameters, specifically: the process parameters comprise an influence factor alpha, an evaporation rate rho, a constant factor C, an iteration control parameter k and a connection matrix gamma; is connected withThe initialized value of the connection matrix gamma is recorded as gamma0(ii) a The initialization formula used is:
α=0.41
ρ=0.6
C=0.0001
k=0
γ0=03×3;
the module 205 updates the process parameters, specifically: the updated value of the k +1 th step of the connection matrix gamma is recorded as gammak+1The element in the first row and the m column is marked asThe update formula used is:
wherein:
t is the sampling interval of the signal sequence S,
updating the k-th step of the connection matrix gamma with the new value gammakThe row l and the column m elements,
l is 1,2,3 is a connection row number,
m is 1,2 and 3 are connecting column serial numbers;
the module 206 calculates an update difference and finishes updating, specifically: the updated difference is recorded as epsilon, and the solving formula is as follows:
ε=|γk+1-γk|
if the difference epsilon is updatedIf the formula epsilon is more than or equal to 0.001, adding 1 to the value of the iteration control parameter k, and returning to the module 205 and the module 206 for updating again; otherwise, iteration is finished and a filter factor omega is obtainedlmHas a value of
The module 207 filters the N window delay cyclic sub-matrices, specifically: filtering the K window cyclic delay sub-matrix, and recording the filtered result as yKThe filtering formula is as follows:
wherein:
the module 208 calculates ant colony optimization values for N windows, specifically: the K window ant colony optimization value is recorded as HKThe formula used is:
HK=||YG||F
wherein:
||YG||Frepresenting the Frobenius norm of the matrix YG,
σ is the mean square error of the signal sequence S;
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN202011229107.5A CN112307999B (en) | 2020-11-06 | 2020-11-06 | Transformer running state vibration and noise detection method and system based on ant colony optimization |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN202011229107.5A CN112307999B (en) | 2020-11-06 | 2020-11-06 | Transformer running state vibration and noise detection method and system based on ant colony optimization |
Publications (2)
Publication Number | Publication Date |
---|---|
CN112307999A CN112307999A (en) | 2021-02-02 |
CN112307999B true CN112307999B (en) | 2021-11-19 |
Family
ID=74326357
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
CN202011229107.5A Expired - Fee Related CN112307999B (en) | 2020-11-06 | 2020-11-06 | Transformer running state vibration and noise detection method and system based on ant colony optimization |
Country Status (1)
Country | Link |
---|---|
CN (1) | CN112307999B (en) |
Citations (3)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN109142946A (en) * | 2018-06-29 | 2019-01-04 | 东华大学 | Transformer fault detection method based on ant group algorithm optimization random forest |
CN109214527A (en) * | 2018-08-09 | 2019-01-15 | 南瑞集团有限公司 | A kind of transformer fault early diagnosis method for early warning and system |
CN111624522A (en) * | 2020-05-29 | 2020-09-04 | 上海海事大学 | Ant colony optimization-based RBF neural network control transformer fault diagnosis method |
Family Cites Families (2)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US8660322B2 (en) * | 2011-08-25 | 2014-02-25 | King Saud University | Passive continuous authentication method |
US9536192B2 (en) * | 2014-06-23 | 2017-01-03 | International Business Machines Corporation | Solving vehicle routing problems using evolutionary computing techniques |
-
2020
- 2020-11-06 CN CN202011229107.5A patent/CN112307999B/en not_active Expired - Fee Related
Patent Citations (3)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN109142946A (en) * | 2018-06-29 | 2019-01-04 | 东华大学 | Transformer fault detection method based on ant group algorithm optimization random forest |
CN109214527A (en) * | 2018-08-09 | 2019-01-15 | 南瑞集团有限公司 | A kind of transformer fault early diagnosis method for early warning and system |
CN111624522A (en) * | 2020-05-29 | 2020-09-04 | 上海海事大学 | Ant colony optimization-based RBF neural network control transformer fault diagnosis method |
Non-Patent Citations (1)
Title |
---|
基于无线数据网的高压输电线路智能检测系统;翟明岳;《电力设备》;20040815(第08期);全文 * |
Also Published As
Publication number | Publication date |
---|---|
CN112307999A (en) | 2021-02-02 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
CN104101833A (en) | Broken rotor bar detection based on current signature analysis of an electric machine | |
CN108535636A (en) | A kind of analog circuit is distributed the neighbouring embedded fault signature extracting method that the victor is a king based on stochastic parameter | |
CN109507554A (en) | A kind of insulation of electrical installation state evaluating method | |
CN106382981B (en) | A kind of single station infrasound signal identification extracting method | |
CN110703149B (en) | Method and system for detecting vibration and sound of running state of transformer by utilizing character spacing | |
US20180180671A1 (en) | Power drive transistor resonance sensor | |
CN111780867A (en) | Transformer running state vibration and sound detection method and system based on Frobenius mode optimization | |
CN112307999B (en) | Transformer running state vibration and noise detection method and system based on ant colony optimization | |
CN115219015A (en) | Transformer fault voiceprint identification method based on multi-dimensional time-frequency characteristics | |
CN111664934A (en) | Transformer state vibration and sound detection signal filtering method and system using feature selection | |
CN116682458A (en) | GIS partial discharge voiceprint detection method based on improved wavelet packet of energy operator | |
CN112254808B (en) | Method and system for detecting vibration and sound of running state of transformer by utilizing gradient change | |
CN111664933A (en) | Method and system for filtering vibration and sound detection signal by utilizing static vector optimization | |
CN111220061A (en) | Fault diagnosis method for magnetic bearing displacement sensor | |
CN113268730A (en) | Smart grid false data injection attack detection method based on reinforcement learning | |
CN111649819A (en) | Transformer state vibration and sound detection signal filtering method and system using iteration soft threshold | |
CN110703145A (en) | Transformer vibration sound signal reconstruction method and system by using multiple optimization theories | |
CN110286291B (en) | Method and system for detecting vibration and sound of running state of transformer by using principal components | |
CN117269660A (en) | Fault arc detection method and system based on variation coefficient difference algorithm | |
CN112327084B (en) | Method and system for detecting vibration and sound of running state of transformer by utilizing equidistant transformation | |
CN112307993B (en) | Method and system for filtering vibration and sound detection signals by using local similarity | |
CN110702215B (en) | Transformer running state vibration and sound detection method and system using regression tree | |
CN115358294A (en) | Micro fault detection method for high-speed train traction system | |
CN110632477A (en) | Transformer running state vibration and sound detection method and system by using Hilbert space factor | |
CN111837119B (en) | Sound signal separation method based on semi-non-negative matrix factorization |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
PB01 | Publication | ||
PB01 | Publication | ||
SE01 | Entry into force of request for substantive examination | ||
SE01 | Entry into force of request for substantive examination | ||
GR01 | Patent grant | ||
GR01 | Patent grant | ||
CF01 | Termination of patent right due to non-payment of annual fee | ||
CF01 | Termination of patent right due to non-payment of annual fee |
Granted publication date: 20211119 |