CN112179479A - Power signal reconstruction method and system by using shaping factor - Google Patents

Power signal reconstruction method and system by using shaping factor Download PDF

Info

Publication number
CN112179479A
CN112179479A CN202011229831.8A CN202011229831A CN112179479A CN 112179479 A CN112179479 A CN 112179479A CN 202011229831 A CN202011229831 A CN 202011229831A CN 112179479 A CN112179479 A CN 112179479A
Authority
CN
China
Prior art keywords
signal sequence
matrix
specifically
formula
approximation
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.)
Pending
Application number
CN202011229831.8A
Other languages
Chinese (zh)
Inventor
翟明岳
魏会娜
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
North China Electric Power University
Original Assignee
North China Electric Power University
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by North China Electric Power University filed Critical North China Electric Power University
Priority to CN202011229831.8A priority Critical patent/CN112179479A/en
Publication of CN112179479A publication Critical patent/CN112179479A/en
Pending legal-status Critical Current

Links

Images

Classifications

    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01H—MEASUREMENT OF MECHANICAL VIBRATIONS OR ULTRASONIC, SONIC OR INFRASONIC WAVES
    • G01H11/00—Measuring mechanical vibrations or ultrasonic, sonic or infrasonic waves by detecting changes in electric or magnetic properties
    • G01H11/06—Measuring mechanical vibrations or ultrasonic, sonic or infrasonic waves by detecting changes in electric or magnetic properties by electric means
    • 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
    • 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
    • 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
    • G06F17/18—Complex mathematical operations for evaluating statistical data, e.g. average values, frequency distributions, probability functions, regression analysis

Abstract

The embodiment of the invention discloses a power signal reconstruction method and a system by using a shaping factor, wherein the method comprises the following steps: step 101, acquiring a signal sequence S acquired according to a time sequence; step 102, obtaining an adjustment coefficient; step 103, solving a power adjustment factor; step 104, obtaining a correction matrix; step 105 initializing iterative process parameters; step 106, updating an approximation vector in an iterative manner; step 107, solving an approximation error and ending the iterative updating process; step 108 finds the reconstructed signal sequence.

Description

Power signal reconstruction method and system by using shaping factor
Technical Field
The invention relates to the field of electric power, in particular to a reconstruction method and a reconstruction system of a vibration sound signal of a transformer.
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. Generally, the abnormal state of the transformer can be divided into core abnormality and winding abnormality. The core abnormality is mainly represented by core saturation, and the winding abnormality generally includes winding deformation, winding looseness and the like.
The basic principle of the transformer abnormal state detection is to extract each characteristic quantity in the operation of the transformer, analyze, identify and track the characteristic quantity so as to monitor the abnormal operation state of the transformer. The detection method can be divided into invasive detection and non-invasive detection according to the contact degree; the detection can be divided into live detection and power failure detection according to whether the shutdown detection is needed or not; the method can be classified into an electrical quantity method, a non-electrical quantity method, and the like according to the type of the detected quantity. In comparison, the non-invasive detection has strong transportability and is more convenient to install; the live detection does not affect the operation of the transformer; the non-electric quantity method is not electrically connected with the power system, so that the method is safer. 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.
In the running process of the transformer, the magnetostriction of the iron core silicon steel sheets and the vibration caused by the winding electrodynamic force can radiate vibration sound signals with different amplitudes and frequencies to the periphery. When the transformer normally operates, uniform low-frequency noise is emitted outwards; if the sound is not uniform, it is not normal. The transformer can make distinctive sounds in different running states, and the running state of the transformer can be mastered by detecting the sounds made by the transformer. It is worth noting that the detection of the sound emitted by the transformer in different operating states not only can detect a plurality of serious faults causing the change of the electrical quantity, but also can detect a plurality of abnormal states which do not endanger the insulation and do not cause the change of the electrical quantity, such as the loosening of internal and external parts of the transformer, and the like.
Disclosure of Invention
As mentioned above, the vibration and sound detection method utilizes the vibration signal emitted by the transformer, which is easily affected by the working environment, resulting in interruption of signal transmission and severe degradation of signal quality, so that the received partial vibration and sound signal cannot be used, and therefore how to effectively reconstruct the vibration and sound signal of the transformer is an important constraint factor for successful application of the method. The existing common method has insufficient attention to the problem, and no effective measure is taken to solve the problem.
The invention aims to provide a power signal reconstruction method and a power signal reconstruction system by using a shaping factor. The method has better signal reconstruction performance and simpler calculation.
In order to achieve the purpose, the invention provides the following scheme:
a method of power signal reconstruction with a shaping factor, comprising:
step 101, acquiring a signal sequence S acquired according to a time sequence;
step 102, obtaining an adjustment coefficient, specifically: the adjustment coefficient is denoted as M, and the solving formula is:
Figure BDA0002764570780000021
wherein:
n is the length of the signal sequence S,
the SNR is the signal-to-noise ratio of the signal sequence S,
Figure BDA0002764570780000022
presentation pair
Figure BDA0002764570780000023
The lower part is taken as the whole,
max | S | represents the maximum of the absolute values of all elements in the signal sequence S,
min | S | represents the minimum of the absolute values of all elements in the signal sequence S;
step 103, solving a power adjustment factor, specifically: the power adjustment factor is denoted as p, and the formula used is:
Figure BDA0002764570780000024
wherein:
σ0is the mean square error of the signal sequence S,
t is the sampling interval of the signal sequence S;
step 104, obtaining a correction matrix, specifically: the correction matrix is denoted as D, and the solving formula is as follows:
D＝UΩ*V
wherein:
u is a matrix [ S-m0][S-m0]TThe left feature matrix of (a) is,
v is a matrix [ S-m ]0][S-m0]TThe right feature matrix of (a) is,
Figure BDA0002764570780000025
to modify the eigenvalue matrix, the solving formula used is:
Figure BDA0002764570780000026
Ωiiis a matrix [ S-m0][S-m0]TThe value of the ith characteristic of (a),
m0is the mean value of the signal sequence S,
i is 1,2, and N is a characteristic value serial number;
step 105 initializes iterative process parameters, specifically: the iterative process parameters comprise an approximation vector d and an iterative control parameter k, and the initialization value of the approximation vector d is recorded as d0The initialization formula used is:
d0＝S
k＝1
step 106, updating the approximation vector iteratively, specifically: the updated value of the step k +1 of the approximation vector d is recorded as dk+1The update formula used is:
Figure BDA0002764570780000031
dk+1＝dk+β[S-Ddk]
wherein:
dkto approximate the kth step update of vector d,
i is an identity matrix and is a matrix of the identity,
beta is a forming factor;
step 107, calculating an approximation error and ending the iterative updating process, specifically: the approximation error is expressed as the formula | | | dk+1-dkL; if the approximation error meets the formula of more than or equal to 0.001, returning to the step 106 and the step 107 to continue the iterative updating process; otherwise, the iterative updating process is ended and the optimal approximate vector d is obtainedOPTHas a value of dOPT＝dk。
Step 108, obtaining a reconstructed signal sequence; the method specifically comprises the following steps: the reconstructed signal sequence is denoted SnewThe reconstruction formula is:
Snew＝dOPT-DS。
a power signal reconstruction system utilizing a shaping factor, comprising:
the module 201 acquires a signal sequence S acquired in time sequence;
the module 202 calculates an adjustment coefficient, specifically: the adjustment coefficient is denoted as M, and the solving formula is:
Figure BDA0002764570780000032
wherein:
n is the length of the signal sequence S,
the SNR is the signal-to-noise ratio of the signal sequence S,
Figure BDA0002764570780000033
presentation pair
Figure BDA0002764570780000034
The lower part is taken as the whole,
max | S | represents the maximum of the absolute values of all elements in the signal sequence S,
min | S | represents the minimum of the absolute values of all elements in the signal sequence S;
the module 203 calculates a power adjustment factor, specifically: the power adjustment factor is denoted as p, and the formula used is:
Figure BDA0002764570780000035
wherein:
σ0is the mean square error of the signal sequence S,
t is the sampling interval of the signal sequence S;
the module 204 calculates a correction matrix, specifically: the correction matrix is denoted as D, and the solving formula is as follows:
D＝UΩ*V
wherein:
u is a matrix [ S-m0][S-m0]TThe left feature matrix of (a) is,
v is a matrix [ S-m ]0][S-m0]TThe right feature matrix of (a) is,
Figure BDA0002764570780000041
to modify the eigenvalue matrix, the solving formula used is:
Figure BDA0002764570780000042
Ωiiis a matrix [ S-m0][S-m0]TThe value of the ith characteristic of (a),
m0is the mean value of the signal sequence S,
i is 1,2, and N is a characteristic value serial number;
the module 205 initializes iterative process parameters, specifically: the iterative process parameters comprise an approximation vector d and an iterative control parameter k, and the initialization value of the approximation vector d is recorded as d0The initialization formula used is:
d0＝S
k＝1
module 206 iteratively updates the approximation vector, specifically: the updated value of the step k +1 of the approximation vector d is recorded as dk+1The update formula used is:
Figure BDA0002764570780000043
dk+1＝dk+β[S-Ddk]
wherein:
dkto approximate the kth step update of vector d,
i is an identity matrix and is a matrix of the identity,
beta is a forming factor;
the module 207 calculates an approximation error and ends the iterative update process, specifically: the approximation error is expressed as the formula | | | dk+1-dkL; if the approximation error satisfies the formula of not less than 0.001, returning to the module 206 and the module 207 to continue the iterative updating process; otherwise, the iterative updating process is ended and the optimal approximate vector d is obtainedOPTHas a value of dOPT＝dk。
The module 208 finds the reconstructed signal sequence; the method specifically comprises the following steps: the reconstructed signal sequence is denoted SnewThe reconstruction formula is:
Snew＝dOPT-DS。
according to the specific embodiment provided by the invention, the invention discloses the following technical effects:
as mentioned above, the vibration and sound detection method utilizes the vibration signal emitted by the transformer, which is easily affected by the working environment, resulting in interruption of signal transmission and severe degradation of signal quality, so that the received partial vibration and sound signal cannot be used, and therefore how to effectively reconstruct the vibration and sound signal of the transformer is an important constraint factor for successful application of the method. The existing common method has insufficient attention to the problem, and no effective measure is taken to solve the problem.
The invention aims to provide a power signal reconstruction method and a power signal reconstruction system by using a shaping factor. The method has better signal reconstruction performance 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 flow chart of a power signal reconstruction method using a shaping factor
Fig. 1 is a flow chart illustrating a power signal reconstruction method using a shaping factor according to the present invention. As shown in fig. 1, the method for reconstructing a power signal by using a shaping factor specifically includes the following steps:
step 101, acquiring a signal sequence S acquired according to a time sequence;
step 102, obtaining an adjustment coefficient, specifically: the adjustment coefficient is denoted as M, and the solving formula is:
Figure BDA0002764570780000051
wherein:
n is the length of the signal sequence S,
the SNR is the signal-to-noise ratio of the signal sequence S,
Figure BDA0002764570780000052
presentation pair
Figure BDA0002764570780000053
The lower part is taken as the whole,
max | S | represents the maximum of the absolute values of all elements in the signal sequence S,
min | S | represents the minimum of the absolute values of all elements in the signal sequence S;
step 103, solving a power adjustment factor, specifically: the power adjustment factor is denoted as p, and the formula used is:
Figure BDA0002764570780000061
wherein:
σ0is the mean square error of the signal sequence S,
t is the sampling interval of the signal sequence S;
step 104, obtaining a correction matrix, specifically: the correction matrix is denoted as D, and the solving formula is as follows:
D＝UΩ*V
wherein:
u is a matrix [ S-m0][S-m0]TThe left feature matrix of (a) is,
v is a matrix [ S-m ]0][S-m0]TThe right feature matrix of (a) is,
Figure BDA0002764570780000062
to modify the eigenvalue matrix, the solving formula used is:
Figure BDA0002764570780000063
Ωiiis a matrix [ S-m0][S-m0]TThe value of the ith characteristic of (a),
m0is the mean value of the signal sequence S,
i is 1,2, and N is a characteristic value serial number;
step 105 initializes iterative process parameters, specifically: the iterative process parameters comprise an approximation vector d and an iterative control parameter k, and the initialization value of the approximation vector d is recorded as d0The initialization formula used is:
d0＝S
k＝1
step 106, updating the approximation vector iteratively, specifically: the updated value of the step k +1 of the approximation vector d is recorded as dk+1The update formula used is:
Figure BDA0002764570780000064
dk+1＝dk+β[S-Ddk]
wherein:
dkto approximate the kth step update of vector d,
i is an identity matrix and is a matrix of the identity,
beta is a forming factor;
step 107, calculating an approximation error and ending the iterative updating process, specifically: the approximation error is expressed as the formula | | | dk+1-dkL; if the approximation error meets the formula of more than or equal to 0.001, returning to the step 106 and the step 107 to continue the iterative updating process; otherwise, the iterative updating process is ended and the optimal approximate vector d is obtainedOPTHas a value of dOPT＝dk。
Step 108, obtaining a reconstructed signal sequence; the method specifically comprises the following steps: the reconstructed signal sequence is denoted SnewThe reconstruction formula is:
Snew＝dOPT-DS。
FIG. 2 is a schematic diagram of a power signal reconstruction system using a shaping factor
Fig. 2 is a schematic structural diagram of a power signal reconstruction system using a shaping factor according to the present invention. As shown in fig. 2, the power signal reconstruction system using the shaping factor includes the following structures:
the module 201 acquires a signal sequence S acquired in time sequence;
the module 202 calculates an adjustment coefficient, specifically: the adjustment coefficient is denoted as M, and the solving formula is:
Figure BDA0002764570780000071
wherein:
n is the length of the signal sequence S,
the SNR is the signal-to-noise ratio of the signal sequence S,
Figure BDA0002764570780000072
presentation pair
Figure BDA0002764570780000073
The lower part is taken as the whole,
max | S | represents the maximum of the absolute values of all elements in the signal sequence S,
min | S | represents the minimum of the absolute values of all elements in the signal sequence S;
the module 203 calculates a power adjustment factor, specifically: the power adjustment factor is denoted as p, and the formula used is:
Figure BDA0002764570780000074
wherein:
σ0is the mean square error of the signal sequence S,
t is the sampling interval of the signal sequence S;
the module 204 calculates a correction matrix, specifically: the correction matrix is denoted as D, and the solving formula is as follows:
D＝UΩ*V
wherein:
u is a matrix [ S-m0][S-m0]TThe left feature matrix of (a) is,
v is a matrix [ S-m ]0][S-m0]TThe right feature matrix of (a) is,
Figure BDA0002764570780000081
to modify the eigenvalue matrix, the solving formula used is:
Figure BDA0002764570780000082
Ωiiis a matrix [ S-m0][S-m0]TThe value of the ith characteristic of (a),
m0is the mean value of the signal sequence S,
i is 1,2, and N is a characteristic value serial number;
the module 205 initializes iterative process parameters, specifically: the iterative process parameters comprise an approximation vector d and an iterative control parameter k, and the initialization value of the approximation vector d is recorded as d0The initialization formula used is:
d0＝S
k＝1
module 206 iteratively updates the approximation vector, specifically: the updated value of the step k +1 of the approximation vector d is recorded as dk+1The update formula used is:
Figure BDA0002764570780000083
dk+1＝dk+β[S-Ddk]
wherein:
dkto approximate the kth step update of vector d,
i is an identity matrix and is a matrix of the identity,
beta is a forming factor;
the module 207 calculates an approximation error and ends the iterative update process, specifically: the approximation error is expressed as the formula | | | dk+1-dkL; if the approximation error satisfies the formula of not less than 0.001, returning to the module 206 and the module 207 to continue the iterative updating process; otherwise, the iterative updating process is ended and the optimal approximate vector d is obtainedOPTHas a value of dOPT＝dk。
The module 208 finds the reconstructed signal sequence; the method specifically comprises the following steps: the reconstructed signal sequence is denoted SnewThe reconstruction formula is:
Snew＝dOPT-DS。
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, obtaining an adjustment coefficient, specifically: the adjustment coefficient is denoted as M, and the solving formula is:
Figure BDA0002764570780000091
wherein:
n is the length of the signal sequence S,
the SNR is the signal-to-noise ratio of the signal sequence S,
Figure BDA0002764570780000092
presentation pair
Figure BDA0002764570780000093
The lower part is taken as the whole,
max | S | represents the maximum of the absolute values of all elements in the signal sequence S,
min | S | represents the minimum of the absolute values of all elements in the signal sequence S;
step 303, solving a power adjustment factor, specifically: the power adjustment factor is denoted as p, and the formula used is:
Figure BDA0002764570780000094
wherein:
σ0is the mean square error of the signal sequence S,
t is the sampling interval of the signal sequence S;
step 304, obtaining a correction matrix, specifically: the correction matrix is denoted as D, and the solving formula is as follows:
D＝UΩ*V
wherein:
u is a matrix [ S-m0][S-m0]TThe left feature matrix of (a) is,
v is a matrix [ S-m ]0][S-m0]TThe right feature matrix of (a) is,
Figure BDA0002764570780000095
to modify the eigenvalue matrix, the solving formula used is:
Figure BDA0002764570780000096
Ωiiis a matrix [ S-m0][S-m0]TThe value of the ith characteristic of (a),
m0is the mean value of the signal sequence S,
i is 1,2, and N is a characteristic value serial number;
step 305 initializes iterative process parameters, specifically: stackThe generation process parameters comprise an approximation vector d and an iteration control parameter k, and the initialization value of the approximation vector d is recorded as d0The initialization formula used is:
d0＝S
k＝1
step 306, iteratively updating the approximation vector, specifically: the updated value of the step k +1 of the approximation vector d is recorded as dk+1The update formula used is:
Figure BDA0002764570780000101
dk+1＝dk+β[S-Ddk]
wherein:
dkto approximate the kth step update of vector d,
i is an identity matrix and is a matrix of the identity,
beta is a forming factor;
step 307, calculating an approximation error and ending the iterative updating process, specifically: the approximation error is expressed as the formula | | | dk+1-dkL; if the approximation error meets the formula of more than or equal to 0.001, returning to the step 306 and the step 307 to continue the iterative updating process; otherwise, the iterative updating process is ended and the optimal approximate vector d is obtainedOPTHas a value of dOPT＝dk。
Step 308, obtaining a reconstructed signal sequence; the method specifically comprises the following steps: the reconstructed signal sequence is denoted SnewThe reconstruction formula is:
Snew＝dOPT-DS。
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. The method for reconstructing a power signal by using a shaping factor includes:
step 101, acquiring a signal sequence S acquired according to a time sequence;
step 102, obtaining an adjustment coefficient, specifically: the adjustment coefficient is denoted as M, and the solving formula is:
Figure FDA0002764570770000011
wherein:
n is the length of the signal sequence S,
the SNR is the signal-to-noise ratio of the signal sequence S,
Figure FDA0002764570770000012
presentation pair
Figure FDA0002764570770000013
The lower part is taken as the whole,
max | S | represents the maximum of the absolute values of all elements in the signal sequence S,
min | S | represents the minimum of the absolute values of all elements in the signal sequence S;
step 103, solving a power adjustment factor, specifically: the power adjustment factor is denoted as p, and the formula used is:
Figure FDA0002764570770000014
wherein:
σ0is the mean square error of the signal sequence S,
t is the sampling interval of the signal sequence S;
step 104, obtaining a correction matrix, specifically: the correction matrix is denoted as D, and the solving formula is as follows:
D＝UΩ*V
wherein:
u is a matrix [ S-m0][S-m0]TThe left feature matrix of (a) is,
v is a matrix [ S-m ]0][S-m0]TThe right feature matrix of (a) is,
Figure FDA0002764570770000015
to modify the eigenvalue matrix, the solving formula used is:
Figure FDA0002764570770000016
Ωiiis a matrix [ S-m0][S-m0]TThe value of the ith characteristic of (a),
m0is the mean value of the signal sequence S,
i is 1,2, and N is a characteristic value serial number;
step 105 initializes iterative process parameters, specifically: the iterative process parameters comprise an approximation vector d and an iterative control parameter k, and the initialization value of the approximation vector d is recorded as d0The initialization formula used is:
d0＝S
k＝1
step 106, updating the approximation vector iteratively, specifically: the updated value of the step k +1 of the approximation vector d is recorded as dk+1The update formula used is:
Figure FDA0002764570770000021
dk+1＝dk+β[S-Ddk]
wherein:
dkto approximate the kth step update of vector d,
i is an identity matrix and is a matrix of the identity,
beta is a forming factor;
step 107, calculating an approximation error and ending the iterative updating process, specifically: the approximation error is expressed as the formula | | | dk+1-dkL; if the approximation error meets the formula of more than or equal to 0.001, returning to the step 106 and the step 107 to continue the iterative updating process; otherwise, the iterative updating process is ended and the optimal approximate vector d is obtainedOPTHas a value of dOPT＝dk。
Step 108, obtaining a reconstructed signal sequence; the method specifically comprises the following steps: the reconstructed signal sequence is denoted SnewThe reconstruction formula is:
Snew＝dOPT-DS。
2. the power signal reconstruction system using a shaping factor includes:
the module 201 acquires a signal sequence S acquired in time sequence;
the module 202 calculates an adjustment coefficient, specifically: the adjustment coefficient is denoted as M, and the solving formula is:
Figure FDA0002764570770000022
wherein:
n is the length of the signal sequence S,
the SNR is the signal-to-noise ratio of the signal sequence S,
Figure FDA0002764570770000023
presentation pair
Figure FDA0002764570770000024
The lower part is taken as the whole,
max | S | represents the maximum of the absolute values of all elements in the signal sequence S,
min | S | represents the minimum of the absolute values of all elements in the signal sequence S;
the module 203 calculates a power adjustment factor, specifically: the power adjustment factor is denoted as p, and the formula used is:
Figure FDA0002764570770000025
wherein:
σ0is the mean square error of the signal sequence S,
t is the sampling interval of the signal sequence S;
the module 204 calculates a correction matrix, specifically: the correction matrix is denoted as D, and the solving formula is as follows:
D＝UΩ*V
wherein:
u is a matrix [ S-m0][S-m0]TThe left feature matrix of (a) is,
v is a matrix [ S-m ]0][S-m0]TThe right feature matrix of (a) is,
Figure FDA0002764570770000031
to modify the eigenvalue matrix, the solving formula used is:
Figure FDA0002764570770000032
Ωiiis a matrix [ S-m0][S-m0]TThe value of the ith characteristic of (a),
m0is the mean value of the signal sequence S,
i is 1,2, and N is a characteristic value serial number;
the module 205 initializes iterative process parameters, specifically: the iterative process parameters include an approximation vectord and an iteration control parameter k, and the initialized value of the approximation vector d is recorded as d0The initialization formula used is:
d0＝S
k＝1
module 206 iteratively updates the approximation vector, specifically: the updated value of the step k +1 of the approximation vector d is recorded as dk+1The update formula used is:
Figure FDA0002764570770000033
dk+1＝dk+β[S-Ddk]
wherein:
dkto approximate the kth step update of vector d,
i is an identity matrix and is a matrix of the identity,
beta is a forming factor;
the module 207 calculates an approximation error and ends the iterative update process, specifically: the approximation error is expressed as the formula | | | dk+1-dkL; if the approximation error satisfies the formula of not less than 0.001, returning to the module 206 and the module 207 to continue the iterative updating process; otherwise, the iterative updating process is ended and the optimal approximate vector d is obtainedOPTHas a value of dOPT＝dk。
The module 208 finds a reconstructed signal sequence; the method specifically comprises the following steps: the reconstructed signal sequence is denoted SnewThe reconstruction formula is:
Snew＝dOPT-DS。
CN202011229831.8A 2020-11-06 2020-11-06 Power signal reconstruction method and system by using shaping factor Pending CN112179479A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202011229831.8A CN112179479A (en) 2020-11-06 2020-11-06 Power signal reconstruction method and system by using shaping factor

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202011229831.8A CN112179479A (en) 2020-11-06 2020-11-06 Power signal reconstruction method and system by using shaping factor

Publications (1)

Publication Number Publication Date
CN112179479A true CN112179479A (en) 2021-01-05

Family

ID=73916414

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202011229831.8A Pending CN112179479A (en) 2020-11-06 2020-11-06 Power signal reconstruction method and system by using shaping factor

Country Status (1)

Country Link
CN (1) CN112179479A (en)

Citations (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20130239653A1 (en) * 2012-03-13 2013-09-19 Snecma System for detecting defects on an aircraft engine impeller wheel
CN105245474A (en) * 2015-10-23 2016-01-13 郑州联睿电子科技有限公司 Ultra-wideband channel estimation method
CN108918928A (en) * 2018-09-11 2018-11-30 广东石油化工学院 Power signal self-adapting reconstruction method in a kind of load decomposition
CN109117450A (en) * 2018-08-04 2019-01-01 华北水利水电大学 The determination method for measured data optimized analysis length of shaking
CN110504678A (en) * 2019-07-14 2019-11-26 广东石油化工学院 A kind of power signal reconstructing method and system based on prediction matrix
CN110572189A (en) * 2019-09-20 2019-12-13 广东石油化工学院 PLC signal filtering method and system utilizing Fermal theory
CN110703145A (en) * 2019-09-20 2020-01-17 广东石油化工学院 Transformer vibration sound signal reconstruction method and system by using multiple optimization theories
US20200200813A1 (en) * 2018-12-21 2020-06-25 Zhejiang University Online diagnosis method for deformation position on trasnformation winding
CN111473861A (en) * 2020-06-04 2020-07-31 广东石油化工学院 Transformer state vibration and sound detection signal reconstruction method and system by using sparse errors
CN111879403A (en) * 2020-08-12 2020-11-03 广东石油化工学院 Vibration and sound detection signal reconstruction method and system by using weak signal retention

Patent Citations (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20130239653A1 (en) * 2012-03-13 2013-09-19 Snecma System for detecting defects on an aircraft engine impeller wheel
CN105245474A (en) * 2015-10-23 2016-01-13 郑州联睿电子科技有限公司 Ultra-wideband channel estimation method
CN109117450A (en) * 2018-08-04 2019-01-01 华北水利水电大学 The determination method for measured data optimized analysis length of shaking
CN108918928A (en) * 2018-09-11 2018-11-30 广东石油化工学院 Power signal self-adapting reconstruction method in a kind of load decomposition
US20200200813A1 (en) * 2018-12-21 2020-06-25 Zhejiang University Online diagnosis method for deformation position on trasnformation winding
CN110504678A (en) * 2019-07-14 2019-11-26 广东石油化工学院 A kind of power signal reconstructing method and system based on prediction matrix
CN110572189A (en) * 2019-09-20 2019-12-13 广东石油化工学院 PLC signal filtering method and system utilizing Fermal theory
CN110703145A (en) * 2019-09-20 2020-01-17 广东石油化工学院 Transformer vibration sound signal reconstruction method and system by using multiple optimization theories
CN111473861A (en) * 2020-06-04 2020-07-31 广东石油化工学院 Transformer state vibration and sound detection signal reconstruction method and system by using sparse errors
CN111879403A (en) * 2020-08-12 2020-11-03 广东石油化工学院 Vibration and sound detection signal reconstruction method and system by using weak signal retention

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
HUANG LINGJIE 等: "Research on the model of HV SF6 circuit breaker fault diagnosis based on fuzzy theory", 《2008 INTERNATIONAL CONFERENCE ON CONDITION MONITORING AND DIAGNOSIS》 *
吴烁民 等: "计及低频采样用电量信号的电器运行情况识别方法", 《电力系统自动化》 *

Similar Documents

Publication Publication Date Title
CN111664934A (en) Transformer state vibration and sound detection signal filtering method and system using feature selection
CN111664933A (en) Method and system for filtering vibration and sound detection signal by utilizing static vector optimization
CN111665405A (en) Vibration and sound detection signal filtering method and system based on sparsity minimization
CN110703145B (en) Transformer vibration sound signal reconstruction method and system by using multiple optimization theories
CN111780868A (en) Transformer running state vibration and noise detection method and system by utilizing Jeffery difference
CN112179479A (en) Power signal reconstruction method and system by using shaping factor
CN110646691B (en) Transformer vibration sound signal filtering method and system by utilizing stretching transformation
CN111473861A (en) Transformer state vibration and sound detection signal reconstruction method and system by using sparse errors
CN112304419A (en) Vibration and sound detection signal reconstruction method and system by using generalized sparse coding
CN111751098A (en) Vibration and sound detection signal reconstruction method and system by using Gaussian prediction model
CN111879403A (en) Vibration and sound detection signal reconstruction method and system by using weak signal retention
CN110837013A (en) Transformer state vibration and sound detection signal reconstruction method and system represented by sparse dictionary
CN111141384A (en) Transformer state vibration and sound detection signal reconstruction method and system by utilizing Frechet regularization
CN110286287B (en) Wavelet transform-based method and system for filtering vibration and sound detection signals of running state of transformer
CN112284520B (en) Vibration and sound detection signal reconstruction method and system by using optimal rank approximation
CN112345226B (en) Vibration and sound detection signal reconstruction method and system by utilizing block coordination minimization
CN110286289B (en) Filtering method for vibration and sound detection signal of transformer
CN111649819A (en) Transformer state vibration and sound detection signal filtering method and system using iteration soft threshold
CN110514295B (en) Transformer running state vibration and sound detection signal filtering method and system by utilizing SVD (singular value decomposition)
CN112284710A (en) Vibration and sound detection signal reconstruction method and system by using European optimal approximation
CN112417994B (en) Vibration and sound detection signal filtering method and system using regularization factor
CN110703148A (en) Transformer vibration sound signal reconstruction method and system by using Hais matrix
CN112179485A (en) Method and system for reconstructing vibration and sound detection signal by using Parseval energy approximation
CN111579059A (en) Transformer state vibration and sound detection signal reconstruction method and system by utilizing quadratic approximation
CN112415439B (en) Vibration and sound detection signal filtering method and system using sparse projection

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
AD01 Patent right deemed abandoned
AD01 Patent right deemed abandoned

Effective date of abandoning: 20230228