CN114137444A - Transformer running state monitoring method and system based on acoustic signals - Google Patents
Transformer running state monitoring method and system based on acoustic signals Download PDFInfo
- Publication number
- CN114137444A CN114137444A CN202111434964.3A CN202111434964A CN114137444A CN 114137444 A CN114137444 A CN 114137444A CN 202111434964 A CN202111434964 A CN 202111434964A CN 114137444 A CN114137444 A CN 114137444A
- Authority
- CN
- China
- Prior art keywords
- acoustic signal
- filter
- transformer
- characteristic coefficient
- covariance matrix
- 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.)
- Granted
Links
- 238000000034 method Methods 0.000 title claims abstract description 31
- 238000012544 monitoring process Methods 0.000 title claims abstract description 29
- 230000005236 sound signal Effects 0.000 claims abstract description 22
- 239000011159 matrix material Substances 0.000 claims description 131
- 239000013598 vector Substances 0.000 claims description 42
- 238000001228 spectrum Methods 0.000 claims description 31
- 238000005316 response function Methods 0.000 claims description 22
- 238000000354 decomposition reaction Methods 0.000 claims description 11
- 238000004364 calculation method Methods 0.000 claims description 10
- 230000002159 abnormal effect Effects 0.000 claims description 9
- 230000017105 transposition Effects 0.000 claims description 6
- 230000003595 spectral effect Effects 0.000 claims description 5
- 230000006870 function Effects 0.000 description 4
- 238000004804 winding Methods 0.000 description 4
- 230000008859 change Effects 0.000 description 3
- 238000010586 diagram Methods 0.000 description 3
- 230000008569 process Effects 0.000 description 3
- 210000005069 ears Anatomy 0.000 description 2
- 238000012423 maintenance Methods 0.000 description 2
- 239000003921 oil Substances 0.000 description 2
- 230000000737 periodic effect Effects 0.000 description 2
- 238000010183 spectrum analysis Methods 0.000 description 2
- 229910000976 Electrical steel Inorganic materials 0.000 description 1
- XEEYBQQBJWHFJM-UHFFFAOYSA-N Iron Chemical group [Fe] XEEYBQQBJWHFJM-UHFFFAOYSA-N 0.000 description 1
- 230000015572 biosynthetic process Effects 0.000 description 1
- 238000001816 cooling Methods 0.000 description 1
- 230000003111 delayed effect Effects 0.000 description 1
- 239000006185 dispersion Substances 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 230000005520 electrodynamics Effects 0.000 description 1
- 238000011418 maintenance treatment Methods 0.000 description 1
- 238000012067 mathematical method Methods 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 230000000750 progressive effect Effects 0.000 description 1
- 238000005070 sampling Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/50—Testing of electric apparatus, lines, cables or components for short-circuits, continuity, leakage current or incorrect line connections
- G01R31/62—Testing of transformers
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01H—MEASUREMENT OF MECHANICAL VIBRATIONS OR ULTRASONIC, SONIC OR INFRASONIC WAVES
- G01H17/00—Measuring mechanical vibrations or ultrasonic, sonic or infrasonic waves, not provided for in the preceding groups
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Engineering & Computer Science (AREA)
- Power Engineering (AREA)
- Measurement Of Mechanical Vibrations Or Ultrasonic Waves (AREA)
Abstract
The invention relates to a method and a system for monitoring the running state of a transformer based on acoustic signals, which belong to the technical field of transformers. The invention monitors the running state of the transformer efficiently and accurately by monitoring the sound signal of the running transformer in real time, thereby being capable of overhauling or replacing the transformer in time and improving the running reliability of the transformer.
Description
Technical Field
The invention relates to the technical field of transformers, in particular to a method and a system for monitoring the running state of a transformer based on an acoustic signal.
Background
The transformer is one of the most important devices in the power system, and the stability of the operation of the transformer has a great influence on the safety of the power system. The transformer is monitored, the running state of the transformer can be mastered in real time, fault early warning is timely carried out, and precaution is carried out in the future. And meanwhile, the operation maintenance and the state maintenance of the transformer are guided, the unplanned power failure is avoided, the service life of the transformer is delayed, and the method has great significance for ensuring the safe and stable operation of the transformer and a power system.
In the running process of the transformer, the winding vibration caused by electrodynamic force and the periodic vibration caused by the magnetostriction of the silicon steel sheets of the iron core are transmitted to the wall of an oil tank of the transformer through a structural part of the transformer and insulating oil, and the periodic vibration and the mechanical vibration and the like caused by a cooling system of the transformer are radiated to the periphery through air to form acoustic signals. Wherein, 20 Hz-20 kHz is the audible frequency range of human ears, and the experienced transformer substation staff can directly judge whether the state is normal by hearing the sound of the running transformer by the ears. Because the audible sound signals can be conveniently acquired through the microphone sensors arranged on the periphery of the transformer and are not in direct contact with the transformer, the method has the characteristics of flexibility and convenience in field implementation, no interference to normal operation of the transformer, strong timeliness and the like, and increasingly attracts attention in the field of transformer state monitoring. However, due to the influence of various factors such as the complexity of the mechanical structure of the transformer, the dispersion of the process, and the diversity of the operating environment of the transformer substation, it is difficult to determine the operating state of the transformer from the acoustic signal analysis of the transformer in operation.
Disclosure of Invention
The invention aims to provide a method and a system for monitoring the running state of a transformer based on an acoustic signal, so as to realize the efficient and accurate judgment of the running state of the transformer by monitoring the acoustic signal of the running transformer in real time.
In order to achieve the purpose, the invention provides the following scheme:
a method of monitoring an operational state of a transformer based on an acoustic signal, the method comprising:
collecting the sound signal of the transformer in operation by using a microphone sensor;
determining a filter bank from the acoustic signal;
taking the acoustic signal as the input of a filter bank, and calculating the characteristic coefficient in the frequency spectrum range of the acoustic signal covered by each filter in the filter bank to form an acoustic signal characteristic coefficient matrix;
determining a covariance matrix of the acoustic signal characteristic coefficient matrix, and calculating a principal component information module value of the covariance matrix;
if the principal component information modulus is larger than or equal to a preset modulus threshold, judging that the running state of the transformer is normal;
and if the principal component information modulus is smaller than a preset modulus threshold, judging that the running state of the transformer is abnormal.
Optionally, determining a filter bank according to the acoustic signal specifically includes:
performing discrete Fourier transform on the acoustic signal to obtain the frequency spectrum distribution of the acoustic signal;
according to the frequency spectrum distribution of the acoustic signal, determining the discrete time response function and the center frequency of each filter in the filter bank as follows:
wherein,
is a discrete time response function of the jth filter, n is time, ajIs the scale factor of the jth filter, aj＝fL/fcj，fLIs the lowest center frequency, f, of the filter bankcjIs the center frequency of the jth filter, b is a time shift factor, α, β are first, second positive real numbers, θ is the initial phase, u () is a unit step function, Q0As a quality factor, B0P is the number of filters for the minimum bandwidth.
Optionally, the acoustic signal is used as an input of a filter bank, and a feature coefficient in a spectral range of the acoustic signal covered by each filter in the filter bank is calculated to form an acoustic signal feature coefficient matrix, which specifically includes:
using the acoustic signal as an input to a filter bank and using a formula
Calculating the characteristic coefficient in the frequency spectrum range of the acoustic signal covered by each filter in the filter bank to form a characteristic coefficient vector of each filter; wherein, Fi(b) Is the characteristic coefficient vector of the ith filter, s (k) is the acoustic signal at the kth time, N is the acoustic signal length,
a discrete time response function corresponding to the acoustic signal at the kth moment of the ith filter;
the characteristic coefficient vector of each filter is used as a row vector, and all the row vectors form an acoustic signal characteristic coefficient matrix.
Optionally, determining a covariance matrix of the acoustic signal feature coefficient matrix, and calculating a principal component information modulus of the covariance matrix, specifically including:
determining the moments of the acoustic signal characteristic coefficientsThe covariance matrix of the array is
Wherein R is a covariance matrix, N is the acoustic signal length, F is an acoustic signal characteristic coefficient matrix, and T represents a matrix transposition;
performing eigenvalue decomposition on the covariance matrix to obtain a plurality of eigenvalues and eigenvectors of the covariance matrix;
using a formula based on a plurality of eigenvalues and eigenvectors of the covariance matrix
Calculating principal component information module values of the covariance matrix; where, ζ is the principal component information modulus, λ1And λWRespectively, the maximum and minimum eigenvalues, | z, of the multiple eigenvalues1I represents a feature vector z1The die of (1).
An acoustic signal based transformer operating condition monitoring system, the system comprising:
the sound signal acquisition module is used for acquiring the sound signal of the transformer in operation by using the microphone sensor;
a filter bank determining module for determining a filter bank according to the acoustic signal;
the acoustic signal characteristic coefficient matrix forming module is used for taking the acoustic signal as the input of a filter bank, calculating characteristic coefficients in the frequency spectrum range of the acoustic signal covered by each filter in the filter bank and forming an acoustic signal characteristic coefficient matrix;
the principal component information module value calculation module is used for determining a covariance matrix of the acoustic signal characteristic coefficient matrix and calculating the principal component information module value of the covariance matrix;
the normal operation judging module is used for judging the operation state of the transformer to be normal if the principal component information modulus is greater than or equal to a preset modulus threshold;
and the abnormal operation judging module is used for judging that the operation state of the transformer is abnormal if the principal component information modulus is smaller than a preset modulus threshold.
Optionally, the filter bank determining module specifically includes:
the frequency spectrum distribution obtaining submodule is used for carrying out discrete Fourier transform on the acoustic signal to obtain the frequency spectrum distribution of the acoustic signal;
a discrete time response function determining submodule, configured to determine, according to the spectral distribution of the acoustic signal, a discrete time response function and a center frequency of each filter in the filter bank as:
wherein,
is a discrete time response function of the jth filter, n is time, ajIs the scale factor of the jth filter, aj＝fL/fcj，fLIs the lowest center frequency, f, of the filter bankcjIs the center frequency of the jth filter, b is a time shift factor, α, β are first, second positive real numbers, θ is the initial phase, u () is a unit step function, Q0As a quality factor, B0P is the number of filters for the minimum bandwidth.
Optionally, the acoustic signal characteristic coefficient matrix forming module specifically includes:
the feature coefficient vector constitutes a submodule for taking the acoustic signal as an input to a filter bank and using a formula
Calculating the characteristic coefficient in the frequency spectrum range of the acoustic signal covered by each filter in the filter bank to form a characteristic coefficient vector of each filter; wherein, Fi(b) Is the characteristic of the ith filterA vector of eigencoefficients, s (k) is the acoustic signal at time k, N is the acoustic signal length,
a discrete time response function corresponding to the acoustic signal at the kth moment of the ith filter;
the acoustic signal characteristic coefficient matrix forming submodule is used for taking the characteristic coefficient vector of each filter as a row vector, and all the row vectors form the acoustic signal characteristic coefficient matrix.
Optionally, the pivot information module value calculating module specifically includes:
a covariance matrix determination submodule for determining a covariance matrix of the acoustic signal feature coefficient matrix as
Wherein R is a covariance matrix, N is the acoustic signal length, F is an acoustic signal characteristic coefficient matrix, and T represents a matrix transposition;
the eigenvalue decomposition submodule is used for performing eigenvalue decomposition on the covariance matrix to obtain a plurality of eigenvalues and eigenvectors of the covariance matrix;
a principal component information module value calculation submodule for utilizing a formula according to a plurality of eigenvalues and eigenvectors of the covariance matrix
Calculating principal component information module values of the covariance matrix; where, ζ is the principal component information modulus, λ1And λWRespectively, the maximum and minimum eigenvalues, | z, of the multiple eigenvalues1I represents a feature vector z1The die of (1).
According to the specific embodiment provided by the invention, the invention discloses the following technical effects:
the invention discloses a method and a system for monitoring the running state of a transformer based on acoustic signals. The invention monitors the running state of the transformer efficiently and accurately by monitoring the sound signal of the running transformer in real time, thereby being capable of overhauling or replacing the transformer in time and improving the running reliability of the transformer.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without inventive exercise.
FIG. 1 is a flow chart of a method for monitoring the operating state of a transformer based on acoustic signals according to the present invention;
FIG. 2 is a schematic diagram of a method for monitoring an operating condition of a transformer based on acoustic signals according to an embodiment of the present invention;
FIG. 3 is a time domain diagram of an acoustic signal provided by an embodiment of the present invention;
fig. 4 is a spectrum distribution diagram of an acoustic signal according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. 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.
The invention aims to provide a method and a system for monitoring the running state of a transformer based on an acoustic signal, so as to realize the efficient and accurate judgment of the running state of the transformer by monitoring the acoustic signal of the running transformer in real time.
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.
The invention provides a transformer running state monitoring method based on acoustic signals, which comprises the following steps of:
The method specifically comprises the following steps:
performing discrete Fourier transform on the acoustic signal to obtain the frequency spectrum distribution of the acoustic signal;
according to the frequency spectrum distribution of the acoustic signal, determining the discrete time response function and the center frequency of each filter in the filter bank as follows:
wherein,
is a discrete time response function of the jth filter, n is time, ajIs the scale factor of the jth filter, aj＝fL/fcj，fLIs the lowest center frequency, f, of the filter bankcjIs the center frequency of the jth filter, b is a time shift factor, α, β are first, second positive real numbers, θ is the initial phase, u () is a unit step function, Q0As a quality factor, B0P is the number of filters for the minimum bandwidth.
And 103, taking the acoustic signal as the input of the filter bank, and calculating the characteristic coefficient in the frequency spectrum range of the acoustic signal covered by each filter in the filter bank to form an acoustic signal characteristic coefficient matrix.
The method specifically comprises the following steps:
using acoustic signals as input to a filter bank and using a formula
Calculating the characteristic coefficient in the frequency spectrum range of the sound signal covered by each filter in the filter group to form the characteristic coefficient vector of each filter; wherein, Fi(b) Is the characteristic coefficient vector of the ith filter, s (k) is the acoustic signal at the kth time, N is the acoustic signal length,
a discrete time response function corresponding to the acoustic signal at the kth moment of the ith filter;
the characteristic coefficient vector of each filter is used as a row vector, and all the row vectors form an acoustic signal characteristic coefficient matrix.
And step 104, determining a covariance matrix of the acoustic signal characteristic coefficient matrix, and calculating a principal component information module value of the covariance matrix.
The method specifically comprises the following steps:
determining a covariance matrix of the acoustic signal eigen coefficient matrix as
Wherein R is a covariance matrix, N is the acoustic signal length, F is an acoustic signal characteristic coefficient matrix, and T represents a matrix transposition;
performing eigenvalue decomposition on the covariance matrix to obtain a plurality of eigenvalues and eigenvectors of the covariance matrix;
using a formula based on a plurality of eigenvalues and eigenvectors of the covariance matrix
Calculating principal component information module values of the covariance matrix; where, ζ is the principal component information modulus, λ1And λWRespectively, the maximum and minimum eigenvalues, | z, of the multiple eigenvalues1I represents a feature vector z1The die of (1).
And 105, if the principal component information modulus is greater than or equal to a preset modulus threshold, judging that the running state of the transformer is normal.
And 106, if the principal component information modulus is smaller than a preset modulus threshold, judging that the running state of the transformer is abnormal.
The invention carries out spectrum analysis on the sound signal of the transformer in a certain time period, designs a filter bank according to the spectrum analysis, constructs a covariance matrix according to a characteristic coefficient matrix of the sound signal after passing through the filter bank, and can judge the working state of the transformer by calculating the change of a principal component information module value of the covariance matrix constructed by the characteristic coefficient matrix of the sound signal.
The method for judging the working state of the transformer directly by monitoring the sound signals of the transformer in real time has the advantages of high efficiency, accuracy and easiness in implementation, and is convenient for operators to find the abnormity of the winding of the transformer in time, so that the transformer is overhauled in time according to the abnormity condition, and the fault damage rate of the transformer is greatly reduced.
To explain the present invention in further detail, the 110kV transformer of a certain substation of a certain power company is taken as an object to perform online monitoring, and as shown in fig. 2, the working state of the transformer is judged according to the following steps:
(1) collecting sound signals s (N) of a transformer in operation by using a microphone sensor according to the national standard GB/T1094.101-2008, wherein the sampling frequency is fsThe acoustic signal length is N; the microphone sensor is connected to a signal acquisition system through a cable to condition, anti-aliasing filter and acquire acoustic signals, the signal acquisition system is connected to a signal analysis terminal through a cable to store and display the acquired transformer acoustic signals, and the method is shown in FIG. 2; here, fs＝51200Hz，N＝102400；
After the sound signal is collected, the sound signal needs to be standardized, and the formula of the standardization is
In the formula,
is the average of the acoustic signal x (t); y (t) is the normalized acoustic signal.
(2) Designing a filter bank according to the frequency spectrum distribution of the acoustic signal, and calculating a characteristic coefficient matrix of the acoustic signal after passing through the filter bank, wherein the calculation process of the characteristic coefficient matrix of the acoustic signal is as follows:
2a, performing discrete Fourier transform on the acoustic signal to obtain the frequency spectrum distribution of the acoustic signal of the transformer, as shown in FIG. 2; the discrete fourier transform described in this step is a mathematical method commonly used in the art, and therefore the inventors will not be described in detail herein;
designing a filter bank according to the spectral distribution of the acoustic signal, wherein the discrete time response function of the jth filter
And corresponding center frequency fcjRespectively as follows:
1≤j≤P
in the formula: f. ofLAnd fHThe lowest central frequency and the cut-off frequency of the filter bank are determined by the frequency spectrum distribution of the sound signal of the transformer; b is a time shift factor that ranges from 1 to N; the filter bank is preferably a cochlear filter bank.
2c, taking the acoustic signal as the input of the filter bank, calculating the characteristic coefficient in the frequency spectrum range covered by each filter, and acquiring an acoustic signal characteristic coefficient matrix FP×NThe number of rows of the acoustic signal characteristic coefficient matrix is P, the number of columns is N (the number of columns of the acoustic signal characteristic coefficient matrix is the same as the length of the acoustic signal), and the kth row vector can be expressed as
(3) Covariance matrix R for calculating acoustic signal characteristic coefficient matrixP×PThe number of rows and columns of the covariance matrix is P, and the calculation formula is
In the formula, T represents a matrix transpose.
(4) Performing eigenvalue decomposition on the covariance matrix of the acoustic signal characteristic parameters to obtain W eigenvalues lambda of the covariance matrix1,λ2,…,λWAnd a feature vector z1,z2,…,zWW eigenvalues of the covariance matrix satisfy lambda1＞λ2＞…＞λW；
(5) Calculating principal component information module value zeta of the covariance matrix of the characteristic parameters of the acoustic signals, wherein the calculation formula is
Wherein, | z1I represents a feature vector z1The die of (1).
(6) And (3) judging the mechanical state of the transformer winding according to the change of the principal component information module value of the covariance matrix of the acoustic signal characteristic parameters of the transformer: if the principal component information modulus zeta is larger than 0.8, judging that the working state of the transformer is normal; if the principal component information modulus zeta is smaller than 0.8, the working state of the transformer is judged to be changed, and the maintenance treatment needs to be carried out in time at the moment, so that the formation of major faults is avoided.
Here, the principal component information modulus ζ of the calculated transformer acoustic signal characteristic parameter covariance matrix is 0.89, which indicates that the transformer operating state is normal.
The invention discloses a method and a system for monitoring an acoustic signal of a transformer in an operation state, which comprises the following steps: collecting a transformer sound signal in operation; carrying out standardization processing on the vibration signal; designing a filter bank according to the frequency spectrum distribution of the acoustic signal, and calculating a characteristic coefficient matrix of the acoustic signal after passing through the filter bank; performing eigenvalue decomposition on the covariance matrix of the acoustic signal characteristic parameter matrix to obtain an eigenvalue of the covariance matrix and a covariance matrix of an eigenvector calculation acoustic signal characteristic coefficient matrix; calculating principal component information module values of the acoustic signal characteristic parameter covariance matrix; and judging the mechanical state of the transformer winding according to the change of the principal component information module value of the covariance matrix of the acoustic signal characteristic parameters of the transformer. The method can effectively and highly sensitively monitor the running state of the transformer on line, thereby timely overhauling or replacing the transformer and improving the running reliability of the transformer.
The invention also provides a system for monitoring the running state of the transformer based on the acoustic signal, which comprises:
the sound signal acquisition module is used for acquiring the sound signal of the transformer in operation by using the microphone sensor;
a filter bank determining module for determining a filter bank according to the acoustic signal;
the acoustic signal characteristic coefficient matrix forming module is used for taking the acoustic signal as the input of the filter bank, calculating characteristic coefficients in the frequency spectrum range of the acoustic signal covered by each filter in the filter bank and forming an acoustic signal characteristic coefficient matrix;
the pivot information module value calculating module is used for determining a covariance matrix of the acoustic signal characteristic coefficient matrix and calculating the pivot information module value of the covariance matrix;
the normal operation judging module is used for judging the operation state of the transformer to be normal if the principal component information modulus is greater than or equal to a preset modulus threshold;
and the abnormal operation judging module is used for judging that the operation state of the transformer is abnormal if the principal component information modulus is smaller than a preset modulus threshold.
The filter bank determining module specifically includes:
the frequency spectrum distribution obtaining submodule is used for carrying out discrete Fourier transform on the acoustic signal to obtain the frequency spectrum distribution of the acoustic signal;
a discrete time response function determining submodule, configured to determine, according to a spectral distribution of the acoustic signal, a discrete time response function and a center frequency of each filter in the filter bank as:
wherein,
is a discrete time response function of the jth filter, n is time, ajIs the scale factor of the jth filter, aj＝fL/fcj，fLIs the lowest center frequency, f, of the filter bankcjIs the center frequency of the jth filter, b is a time shift factor, α, β are first, second positive real numbers, θ is the initial phase, u () is a unit step function, Q0As a quality factor, B0P is the number of filters for the minimum bandwidth.
The acoustic signal characteristic coefficient matrix forming module specifically includes:
the feature coefficient vector constitutes a submodule for taking the acoustic signal as an input to the filter bank and using a formula
Calculating the characteristic coefficient in the frequency spectrum range of the sound signal covered by each filter in the filter group to form the characteristic coefficient vector of each filter; wherein, Fi(b) Is the characteristic coefficient vector of the ith filter, s (k) is the acoustic signal at the kth time, N is the acoustic signal length,
a discrete time response function corresponding to the acoustic signal at the kth moment of the ith filter;
the acoustic signal characteristic coefficient matrix forming submodule is used for taking the characteristic coefficient vector of each filter as a row vector, and all the row vectors form the acoustic signal characteristic coefficient matrix.
The principal component information module value calculation module specifically comprises:
a covariance matrix determination submodule for determining a covariance matrix of the acoustic signal eigen coefficient matrix as
Wherein R is a covariance matrix, N is the column number of the covariance matrix, F is an acoustic signal characteristic coefficient matrix, and T represents a matrix transposition;
the eigenvalue decomposition submodule is used for performing eigenvalue decomposition on the covariance matrix to obtain a plurality of eigenvalues and eigenvectors of the covariance matrix;
a principal component information module value calculation submodule for utilizing a formula according to a plurality of eigenvalues and eigenvectors of the covariance matrix
Calculating principal component information module values of the covariance matrix; where, ζ is the principal component information modulus, λ1And λWRespectively, the maximum and minimum eigenvalues, | z, of the multiple eigenvalues1I represents a feature vector z1The die of (1).
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 relatively simple because the system corresponds to the method disclosed by the embodiment, and the relevant points 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 (8)
1. A method for monitoring the running state of a transformer based on an acoustic signal is characterized by comprising the following steps:
collecting the sound signal of the transformer in operation by using a microphone sensor;
determining a filter bank from the acoustic signal;
taking the acoustic signal as the input of a filter bank, and calculating the characteristic coefficient in the frequency spectrum range of the acoustic signal covered by each filter in the filter bank to form an acoustic signal characteristic coefficient matrix;
determining a covariance matrix of the acoustic signal characteristic coefficient matrix, and calculating a principal component information module value of the covariance matrix;
if the principal component information modulus is larger than or equal to a preset modulus threshold, judging that the running state of the transformer is normal;
and if the principal component information modulus is smaller than a preset modulus threshold, judging that the running state of the transformer is abnormal.
2. The method for monitoring the operating state of the transformer based on the acoustic signal according to claim 1, wherein determining the filter bank according to the acoustic signal specifically comprises:
performing discrete Fourier transform on the acoustic signal to obtain the frequency spectrum distribution of the acoustic signal;
according to the frequency spectrum distribution of the acoustic signal, determining the discrete time response function and the center frequency of each filter in the filter bank as follows:
wherein,
is a discrete time response function of the jth filter, n is time, ajIs the scale factor of the jth filter, aj＝fL/fcj，fLIs the lowest center frequency, f, of the filter bankcjIs the center frequency of the jth filter, b is a time shift factor, α, β are first, second positive real numbers, θ is the initial phase, u () is a unit step function, Q0As a quality factor, B0P is the number of filters for the minimum bandwidth.
3. The method for monitoring the operating state of the transformer based on the acoustic signal according to claim 1, wherein the acoustic signal is used as an input of a filter bank, and the characteristic coefficients in the frequency spectrum range of the acoustic signal covered by each filter in the filter bank are calculated to form an acoustic signal characteristic coefficient matrix, and specifically comprises:
using the acoustic signal as an input to a filter bank and using a formula
Calculating the characteristic coefficient in the frequency spectrum range of the acoustic signal covered by each filter in the filter bank to form a characteristic coefficient vector of each filter; wherein, Fi(b) Is the characteristic coefficient vector of the ith filter, s (k) is the acoustic signal at the k time, N is the acoustic signal length, Ψai,b(k) A discrete time response function corresponding to the acoustic signal at the kth moment of the ith filter;
the characteristic coefficient vector of each filter is used as a row vector, and all the row vectors form an acoustic signal characteristic coefficient matrix.
4. The method for monitoring the operating state of the acoustic signal-based transformer according to claim 1, wherein a covariance matrix of the acoustic signal characteristic coefficient matrix is determined, and a principal component information modulus of the covariance matrix is calculated, and specifically comprises:
determining a covariance matrix of the acoustic signal eigen coefficient matrix as
Wherein R is a covariance matrix, N is the acoustic signal length, F is an acoustic signal characteristic coefficient matrix, and T represents a matrix transposition;
performing eigenvalue decomposition on the covariance matrix to obtain a plurality of eigenvalues and eigenvectors of the covariance matrix;
using a formula based on a plurality of eigenvalues and eigenvectors of the covariance matrix
Calculating principal component information module values of the covariance matrix; where, ζ is the principal component information modulus, λ1And λWRespectively, the maximum and minimum eigenvalues, | z, of the multiple eigenvalues1I represents a feature vector z1The die of (1).
5. An acoustic signal based transformer operating condition monitoring system, the system comprising:
the sound signal acquisition module is used for acquiring the sound signal of the transformer in operation by using the microphone sensor;
a filter bank determining module for determining a filter bank according to the acoustic signal;
the acoustic signal characteristic coefficient matrix forming module is used for taking the acoustic signal as the input of a filter bank, calculating characteristic coefficients in the frequency spectrum range of the acoustic signal covered by each filter in the filter bank and forming an acoustic signal characteristic coefficient matrix;
the principal component information module value calculation module is used for determining a covariance matrix of the acoustic signal characteristic coefficient matrix and calculating the principal component information module value of the covariance matrix;
the normal operation judging module is used for judging the operation state of the transformer to be normal if the principal component information modulus is greater than or equal to a preset modulus threshold;
and the abnormal operation judging module is used for judging that the operation state of the transformer is abnormal if the principal component information modulus is smaller than a preset modulus threshold.
6. The system for monitoring the operating condition of the transformer based on the acoustic signal according to claim 5, wherein the filter bank determining module specifically comprises:
the frequency spectrum distribution obtaining submodule is used for carrying out discrete Fourier transform on the acoustic signal to obtain the frequency spectrum distribution of the acoustic signal;
a discrete time response function determining submodule, configured to determine, according to the spectral distribution of the acoustic signal, a discrete time response function and a center frequency of each filter in the filter bank as:
wherein,
is a discrete time response function of the jth filter, n is time, ajIs the scale factor of the jth filter, aj＝fL/fcj，fLIs the lowest center frequency, f, of the filter bankcjIs the center frequency of the jth filter, b is a time shift factor, α, β are first, second positive real numbers, θ is the initial phase, u () is a unit step function, Q0As a quality factor, B0P is the number of filters for the minimum bandwidth.
7. The system for monitoring the operating condition of the transformer based on the acoustic signal according to claim 5, wherein the acoustic signal characteristic coefficient matrix forming module specifically comprises:
the feature coefficient vector constitutes a submodule for taking the acoustic signal as an input to a filter bank and using a formula
Calculating the characteristic coefficient in the frequency spectrum range of the acoustic signal covered by each filter in the filter bank to form a characteristic coefficient vector of each filter; wherein, Fi(b) Is the characteristic coefficient vector of the ith filter, s (k) is the acoustic signal at the k time, N is the acoustic signal length, Ψai,b(k) A discrete time response function corresponding to the acoustic signal at the kth moment of the ith filter;
the acoustic signal characteristic coefficient matrix forming submodule is used for taking the characteristic coefficient vector of each filter as a row vector, and all the row vectors form the acoustic signal characteristic coefficient matrix.
8. The system for monitoring the operating condition of the transformer based on the acoustic signal according to claim 5, wherein the module for calculating the module of the principal component information module specifically comprises:
a covariance matrix determination submodule for determining a covariance matrix of the acoustic signal feature coefficient matrix as
Wherein R is a covariance matrix, N is the acoustic signal length, F is an acoustic signal characteristic coefficient matrix, and T represents a matrix transposition;
the eigenvalue decomposition submodule is used for performing eigenvalue decomposition on the covariance matrix to obtain a plurality of eigenvalues and eigenvectors of the covariance matrix;
a principal component information module value calculation submodule for utilizing a formula according to a plurality of eigenvalues and eigenvectors of the covariance matrix
Calculating principal component information module values of the covariance matrix; where, ζ is the principal component information modulus, λ1And λWRespectively, the maximum and minimum eigenvalues, | z, of the multiple eigenvalues1I represents a feature vector z1The die of (1).
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111434964.3A CN114137444B (en) | 2021-11-29 | 2021-11-29 | Transformer running state monitoring method and system based on acoustic signals |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111434964.3A CN114137444B (en) | 2021-11-29 | 2021-11-29 | Transformer running state monitoring method and system based on acoustic signals |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN114137444A true CN114137444A (en) | 2022-03-04 |
| CN114137444B CN114137444B (en) | 2024-04-02 |
Family
ID=80389312
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111434964.3A Active CN114137444B (en) | 2021-11-29 | 2021-11-29 | Transformer running state monitoring method and system based on acoustic signals |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114137444B (en) |
Citations (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105699839A (en) * | 2016-01-28 | 2016-06-22 | 云南电网有限责任公司电力科学研究院 | A voltage transformer winding working state detection method and system |
| CN107748314A (en) * | 2017-10-18 | 2018-03-02 | 国网重庆市电力公司北碚供电分公司 | Transformer Faults Analysis system based on sound wave shock detection |
| CN109031014A (en) * | 2017-12-28 | 2018-12-18 | 国网湖北省电力公司宜昌供电公司 | A kind of transformer synthesis reliability assessment and prediction technique based on operation data |
| CN109409762A (en) * | 2018-11-07 | 2019-03-01 | 国网四川省电力公司成都供电公司 | Based on the harmonic contributions division methods for improving data relation analysis |
| CN110286291A (en) * | 2019-07-15 | 2019-09-27 | 广东石油化工学院 | A kind of running state of transformer vibration sound detection method and system using principal component |
| CN110376455A (en) * | 2019-06-26 | 2019-10-25 | 深圳供电局有限公司 | Transformer working state detection method and device, computer equipment and storage medium |
| CN110398647A (en) * | 2019-06-26 | 2019-11-01 | 深圳供电局有限公司 | Transformer state monitoring method |
| CN110458976A (en) * | 2019-07-18 | 2019-11-15 | 浙江大学 | A kind of propeller cavitation condition detection method based on small echo and principal component analysis |
| US20200106523A1 (en) * | 2018-09-28 | 2020-04-02 | Fujitsu Limited | Fiber nonlinear noise monitoring method and apparatus and optical receiver |
| CN111077384A (en) * | 2019-10-17 | 2020-04-28 | 华北电力大学（保定） | Online monitoring method for internal faults of transformer |
| CN111664934A (en) * | 2020-07-05 | 2020-09-15 | 广东石油化工学院 | Transformer state vibration and sound detection signal filtering method and system using feature selection |
| CN112119319A (en) * | 2018-05-18 | 2020-12-22 | 赖茵豪森机械制造公司 | State analysis of an inductively operating component |
| CN112395959A (en) * | 2020-10-30 | 2021-02-23 | 天合云能源互联网技术(杭州)有限公司 | Power transformer fault prediction and diagnosis method and system based on audio features |
-
2021
- 2021-11-29 CN CN202111434964.3A patent/CN114137444B/en active Active
Patent Citations (15)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105699839A (en) * | 2016-01-28 | 2016-06-22 | 云南电网有限责任公司电力科学研究院 | A voltage transformer winding working state detection method and system |
| CN107748314A (en) * | 2017-10-18 | 2018-03-02 | 国网重庆市电力公司北碚供电分公司 | Transformer Faults Analysis system based on sound wave shock detection |
| CN109031014A (en) * | 2017-12-28 | 2018-12-18 | 国网湖北省电力公司宜昌供电公司 | A kind of transformer synthesis reliability assessment and prediction technique based on operation data |
| CN112119319A (en) * | 2018-05-18 | 2020-12-22 | 赖茵豪森机械制造公司 | State analysis of an inductively operating component |
| US20200106523A1 (en) * | 2018-09-28 | 2020-04-02 | Fujitsu Limited | Fiber nonlinear noise monitoring method and apparatus and optical receiver |
| CN110971295A (en) * | 2018-09-28 | 2020-04-07 | 富士通株式会社 | Optical fiber nonlinear noise monitoring method and device and optical receiver |
| CN109409762A (en) * | 2018-11-07 | 2019-03-01 | 国网四川省电力公司成都供电公司 | Based on the harmonic contributions division methods for improving data relation analysis |
| CN110398647A (en) * | 2019-06-26 | 2019-11-01 | 深圳供电局有限公司 | Transformer state monitoring method |
| CN110376455A (en) * | 2019-06-26 | 2019-10-25 | 深圳供电局有限公司 | Transformer working state detection method and device, computer equipment and storage medium |
| CN110286291A (en) * | 2019-07-15 | 2019-09-27 | 广东石油化工学院 | A kind of running state of transformer vibration sound detection method and system using principal component |
| CN110458976A (en) * | 2019-07-18 | 2019-11-15 | 浙江大学 | A kind of propeller cavitation condition detection method based on small echo and principal component analysis |
| JP2021018818A (en) * | 2019-07-18 | 2021-02-15 | 浙江大学Ｚｈｅｊｉａｎｇ Ｕｎｉｖｅｒｓｉｔｙ | Propeller cavitation state detection method based on wavelet and principal component analysis |
| CN111077384A (en) * | 2019-10-17 | 2020-04-28 | 华北电力大学（保定） | Online monitoring method for internal faults of transformer |
| CN111664934A (en) * | 2020-07-05 | 2020-09-15 | 广东石油化工学院 | Transformer state vibration and sound detection signal filtering method and system using feature selection |
| CN112395959A (en) * | 2020-10-30 | 2021-02-23 | 天合云能源互联网技术(杭州)有限公司 | Power transformer fault prediction and diagnosis method and system based on audio features |
Non-Patent Citations (2)
| Title |
|---|
| 张东: "基于变分模式分解和微积分增...能量算子的滚动轴承故障诊断", 工程科学学报, vol. 38, no. 9, pages 1327 - 1334 * |
| 顾煜炯: "基于提取振源方向的频谱类型识别", 自动化仪表, vol. 40, no. 2, pages 1 - 7 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN114137444B (en) | 2024-04-02 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN106934421B (en) | Power transformer fault detection method based on 2DPCA and SVM | |
| CN112201260B (en) | Transformer running state online detection method based on voiceprint recognition | |
| CN110221137B (en) | Distribution transformer abnormal state detection method based on vibration-sound correlation | |
| CN116295661B (en) | Transformer fault early warning system based on Internet of things | |
| CN108535638B (en) | On-load tap-changer mechanical state monitoring method and system based on multilayer filter | |
| CN112147444B (en) | Power transformer working state monitoring method and system | |
| CN110398647B (en) | Transformer state monitoring method | |
| CN109507510A (en) | A kind of transformer fault diagnosis system | |
| CN102692887A (en) | Device for monitoring online state of power transformer based on audio identification technology | |
| CN105974273A (en) | Power distribution network fault positioning system | |
| CN108898050A (en) | A kind of flexible material process equipment roll shaft performance index calculation method | |
| CN118278624B (en) | Intelligent analysis system for electric power monitoring data based on mutual inductor | |
| CN114441914A (en) | Switch cabinet partial discharge detection system based on data processing | |
| JP4279763B2 (en) | Power system stability diagnosis device, power system stabilization device, and power system contraction support device | |
| CN107976251A (en) | A kind of transmission pressure structure destroys on-line monitoring system and monitoring method | |
| CN114137444B (en) | Transformer running state monitoring method and system based on acoustic signals | |
| Liu et al. | Dry-type AMDT fault detection based on vibration signal analysis by wavelet packet | |
| CN202281934U (en) | Power transformer on-line state monitoring device based on audio frequency identification technique | |
| CN117116293A (en) | Machine equipment fault diagnosis system in complex sound field environment | |
| CN112327218A (en) | Transformer online monitoring and fault diagnosis method | |
| CN116884432A (en) | VMD-JS divergence-based power transformer fault voiceprint diagnosis method | |
| CN112946531B (en) | Transformer fault diagnosis device and diagnosis method | |
| CN114167316A (en) | Transformer winding mechanical state monitoring method based on manifold distance entropy | |
| CN115754630A (en) | Non-contact partial discharge monitoring method and device based on acoustic array | |
| CN113900050A (en) | Dry-type transformer state monitoring and fault diagnosis device and system based on vibration signals |
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 |