CN111473860B - Method for extracting vibration signal characteristic parameters of high-voltage alternating-current circuit breaker - Google Patents

Method for extracting vibration signal characteristic parameters of high-voltage alternating-current circuit breaker Download PDF

Info

Publication number
CN111473860B
CN111473860B CN202010477132.9A CN202010477132A CN111473860B CN 111473860 B CN111473860 B CN 111473860B CN 202010477132 A CN202010477132 A CN 202010477132A CN 111473860 B CN111473860 B CN 111473860B
Authority
CN
China
Prior art keywords
vibration
data
vibration signal
characteristic parameters
frequency
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.)
Active
Application number
CN202010477132.9A
Other languages
Chinese (zh)
Other versions
CN111473860A (en
Inventor
杨为
朱太云
闫静
朱胜龙
张国宝
赵恒阳
蔡梦怡
陈忠
秦少瑞
赵常威
秦金飞
宋东波
杨海涛
钱宇骋
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.)
Xian Jiaotong University
Electric Power Research Institute of State Grid Anhui Electric Power Co Ltd
Original Assignee
Xian Jiaotong University
Electric Power Research Institute of State Grid Anhui Electric Power Co Ltd
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 Xian Jiaotong University, Electric Power Research Institute of State Grid Anhui Electric Power Co Ltd filed Critical Xian Jiaotong University
Priority to CN202010477132.9A priority Critical patent/CN111473860B/en
Publication of CN111473860A publication Critical patent/CN111473860A/en
Application granted granted Critical
Publication of CN111473860B publication Critical patent/CN111473860B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • 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
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
    • G01M13/00—Testing of machine parts
    • 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
    • G01R31/327—Testing of circuit interrupters, switches or circuit-breakers
    • G01R31/3271—Testing of circuit interrupters, switches or circuit-breakers of high voltage or medium voltage devices
    • G01R31/3272—Apparatus, systems or circuits therefor

Landscapes

  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Measurement Of Mechanical Vibrations Or Ultrasonic Waves (AREA)

Abstract

The invention discloses a method for extracting vibration signal characteristic parameters of a high-voltage alternating-current circuit breaker, and belongs to the field of state monitoring and fault diagnosis of the high-voltage circuit breaker. M characteristic parameter arrays [ I ] of the vibration signal data are obtained by applying cubic spline fitting envelope curve to the vibration signal data and extracting characteristic parameters through a prony algorithm to be combinedmk，fk，ak，tNk]And k is 1,2, …, n. Will f isk、akAs modal parameters, identifying the vibration failure modemkFor evaluating the intensity of vibration of the mode, tNkWhich is used to indicate the moment when the mode vibration fault occurs. The invention can quickly and accurately extract the vibration signal characteristic parameters and carry out mode identification and state evaluation on mechanical faults.

Description

Method for extracting vibration signal characteristic parameters of high-voltage alternating-current circuit breaker
The technical field is as follows:
the invention belongs to the field of state monitoring and fault diagnosis of a high-voltage alternating-current circuit breaker, and particularly relates to a vibration signal characteristic parameter extraction method for the high-voltage alternating-current circuit breaker.
Background art:
the high-voltage alternating-current circuit breaker is an important core device for controlling and protecting a power system, and common faults include mechanical faults, insulation faults and overheating faults. Operational experience has shown that mechanical faults account for about 70% of all faults of a high-voltage ac circuit breaker, mechanical faults are the most dominant faults, and circuit breaker operating mechanism faults account for 37% of major faults. The high-voltage circuit breaker often has the faults of loosening of a base screw, loosening of a soft connection clamping piece at the position of a moving contact, abnormal operation of a lock plunger, jamming of a mechanism and the like. Therefore, the state monitoring and fault diagnosis work of the mechanical fault of the high-voltage alternating-current circuit breaker is actively and effectively carried out, the problems existing in the operation can be found in time, the maintenance or overhaul work is arranged, and the equipment utilization rate and the operation and maintenance management level are improved.
Mechanical failure of a high voltage ac circuit breaker typically results in a plurality of vibration events, and the signal excited by the vibration events may be expressed as a plurality of transient unsteady damped vibration signals, i.e. vibration signals
Figure BDA0002516205630000011
In the formula, AiIs the maximum amplitude of the ith vibration event, aiTo be the attenuation coefficient, fiIs the frequency of vibration, tiε (t) is a step function for the moment of the vibration event. Pass through monitorThe vibration signal of the high-voltage measuring circuit breaker is extracted by applying a proper data analysis algorithm, so that the fault mode can be identified and evaluated, and a reliable basis can be provided for overhaul and maintenance. Therefore, the calculation method for rapidly and accurately acquiring the vibration signal characteristic parameter extraction and the fault mode identification is particularly important, and a reliable basis can be provided for further high-voltage circuit breaker state evaluation.
The invention content is as follows:
the invention aims to provide a method for extracting vibration signal characteristic parameters of a high-voltage alternating-current circuit breaker, which can be used for quickly and accurately extracting the vibration signal characteristic parameters and carrying out mode identification and state evaluation on mechanical faults.
The technical scheme adopted by the invention is as follows:
a vibration signal characteristic parameter extraction method for a high-voltage alternating-current circuit breaker comprises the following steps:
s100, vibration signal data (t) acquired on the time axisi,Ai) Constructing a discrete function, namely:
Ai＝f(ti) i＝1,2,…,N (1)
in the formula, t is time, A is vibration signal amplitude, and N is the number of sampling points;
applying the median filter function g to medfilt (y, n) versus the discrete function Ai＝f(ti) Performing noise elimination processing twice, wherein y is discrete data to be filtered, and n is the size of a neighborhood window;
s200, extracting characteristic parameters by applying cubic spline fitting envelope curve to vibration signal data and prony algorithm to be combined to obtain m characteristic parameter arrays [ I ] of the vibration signal datamk，fk，ak，tNk]，k＝1,2,…,n；
S300, assuming that the calculation is terminated after m operations, obtaining m characteristic parameter arrays, namely: [ I ] ofmk，fk，ak，tNk]K is 1,2, …, m; the characteristic parameter fk、akThe mode of vibration is identified as a mode parameter, assuming a typical vibrationThe standard characteristic parameters of the failure modes are Iref、fref、arefDefining the weighted Euclidean distance lambda as:
Figure BDA0002516205630000021
when lambda is less than or equal to epsilon, the typical vibration fault exists; k is a radical of1、k2Is a weight coefficient, and epsilon is an evaluation deviation;
characteristic parameter ImkAnd IrefRatio of
Figure BDA0002516205630000031
Obtaining two kinds of prompt information of failure and warning as the evaluation index of the vibration signal intensity; characteristic parameter tNkIndicating the time when the vibration fault occurs this time.
The invention further improves the following steps: in step S100, the vibration signal acquisition device is composed of an acceleration sensor, an acquisition card, and an upper computer software system.
The invention further improves the following steps: step S200 specifically includes the following steps:
s201, in the first operation, the discrete function A after noise elimination is carried outi＝f(ti) Intercepting a first section of single-frequency vibration data on a time window, namely:
A1i＝f(ti) i＝N1,2,…,N1end (2)
in the formula, N1To intercept the start position of the sampled data, N1endThe end position of the sample data is intercepted;
applying prony algorithm to single-frequency vibration data A1i＝f(ti) And (3) performing operation, and calculating to obtain the single-frequency data characteristic parameters: initial vibration amplitude Im1Frequency f1Attenuation coefficient a1；
And then obtaining a discrete expression of the single-frequency vibration signal as follows:
Figure BDA0002516205630000032
s202, in the second operation, the discrete data A is collectedi＝f(ti) The first single-frequency vibration signal A 'determined by the first operation is stripped'1i；
And then, intercepting a second section of single-frequency vibration data on a time window, namely:
A2i＝f(ti) i＝N2,2,…,N2end (4)
in the formula, N2To intercept the start position of the sampled data, N2endIntercepting the end position of the sampled data;
applying prony algorithm to single-frequency vibration data A2i＝f(ti) And (3) performing operation, and calculating to obtain the single-frequency data characteristic parameters: initial vibration amplitude Im2Frequency f2Attenuation coefficient a2
And further obtaining an analytic expression of the single-frequency vibration signal as follows:
Figure BDA0002516205630000033
s203, during the k-th operation, the discrete data A is collectedi＝f(ti) K-1 single-frequency vibration signals determined k-1 times before stripping
Figure BDA0002516205630000041
And then intercepting the kth section of single-frequency vibration data on a time window, namely:
Aki＝f(ti) i＝Nk,2,…,Nkend (6)
in the formula, NkTo intercept the start position of the sampled data, NkendIntercepting the end position of the sampled data;
applying prony algorithm to single-frequency vibration data Aki＝f(ti) Performing operation and calculationObtaining the single-frequency data characteristic parameters: initial vibration amplitude ImkFrequency fkAttenuation coefficient ak(ii) a The analytical expression of the single-frequency vibration signal is obtained as follows:
Figure BDA0002516205630000043
setting a threshold value gamma, and setting the initial vibration amplitude I of the k-th operationmk<F, the operation is terminated; otherwise, the calculation process of step S203 is repeated.
The invention further improves the following steps: in S201, N1Is a vibration signal (t)i,Ai) Using cubic spline fitting algorithm to obtain vibration signal Ai＝f(ti) Upper and lower envelope s of the peak1、s2Line of mean value
Figure BDA0002516205630000042
When large fluctuation occurs on the time axis, the sampling point N corresponding to the moment is taken1endTo intercept the end position of the sampled data.
The invention further improves the following steps: in S203, k1、k2The values are respectively 0.9 and 0.1, the value range of epsilon is 0-0.3, or the value is set according to field experience values.
The invention further improves the following steps: in S300, when R is more than or equal to 0.8str<1, sending out a fault signal.
The invention further improves the following steps: when R is more than or equal to 0.2str<A warning signal is sent out when the temperature is 0.5 ℃; when R is more than or equal to 0.5str<At 0.8, a fault or warning signal may be issued based on field experience.
Compared with the prior art, the invention at least has the following technical effects:
the invention relates to a method for extracting the characteristic parameters of a vibration signal of a high-voltage alternating-current circuit breaker, which combines the application of cubic spline fitting envelope curve to the vibration signal data and the extraction of the characteristic parameters by a prony algorithm to obtain m characteristic parameter arrays [ I ] of the vibration signal datamk，fk，ak，tNk]And k is 1,2, …, n. Will f isk、akAs modal parameters, identifying the vibration failure modemkFor evaluating the intensity of vibration of the mode, tNkWhich is used to indicate the moment when the mode vibration fault occurs. The invention can quickly and accurately extract the vibration signal characteristic parameters and carry out mode identification and state evaluation on mechanical faults.
Description of the drawings:
FIG. 1 vibration signal data;
FIG. 2 shows the upper and lower envelope lines and the mean line for the first calculation;
fig. 3 vibration signal vibration mode decomposition results.
Detailed Description
In order to make those skilled in the art better understand the technical solution of the present invention, the technical solution in the embodiment of the present invention will be clearly and completely described below with reference to the drawings in the embodiment of the present invention, and it is obvious that the described embodiment is only a part of the embodiment of the present invention, and not all embodiments. All other embodiments, which can be obtained by a person skilled in the art without any inventive step based on the embodiments of the present invention, shall fall within the scope of protection of the present invention.
A vibration signal characteristic parameter extraction method for a high-voltage alternating-current circuit breaker comprises the following steps:
1) from vibration signal data (t) acquired on the time axisi,Ai) Constructing a discrete function, namely:
Ai＝f(ti) i＝1,2,…,N (1)
in the formula, t is time, A is vibration signal amplitude, and N is the number of sampling points.
Applying the median filter function g to medfilt (y, n) versus the discrete function Ai＝f(ti) And (5) denoising twice, wherein y is discrete data to be filtered, and n is the size of a neighborhood window.
The device for acquiring the vibration signals comprises an acceleration sensor, an acquisition card and an upper computer software system, wherein the acceleration sensor is front-end measurement equipment of the vibration signals, and the vibration signals measured by the acceleration sensor are input into the data acquisition card and transmitted to the upper computer through the acquisition card to acquire and process data.
2) For the first operation, the discrete function A after noise elimination is performedi＝f(ti) Intercepting a first section of single-frequency vibration data on a time window, namely:
A1i＝f(ti) i＝N1,2,…,N1end (2)
in the formula, N1To intercept the start position of the sampled data, N1endThe end position of the sampled data is truncated.
Applying prony algorithm to single-frequency vibration data A1i＝f(ti) And (3) performing operation, and calculating to obtain the single-frequency data characteristic parameters: initial vibration amplitude Im1Frequency f1Attenuation coefficient a1. Further, the discrete expression of the single-frequency vibration signal can be obtained as follows:
Figure BDA0002516205630000061
preferably, in step 2), N1Is a vibration signal (t)i,Ai) Using cubic spline fitting algorithm to obtain vibration signal Ai＝f(ti) Upper and lower envelope s of the peak1、s2Line of mean value
Figure BDA0002516205630000062
When large fluctuation occurs on the time axis, the sampling point N corresponding to the moment is taken1endTo intercept the end position of the sampled data.
3) From the discrete data A collected during the second operationi＝f(ti) The first single-frequency vibration signal A 'determined by the first operation is stripped'1i. Further, intercepting a second segment of single-frequency vibration data on the time window, namely:
A2i＝f(ti) i＝N2,2,…,N2end (4)
in the formula, N2To intercept the start position of the sampled data, N2endTo intercept the end position of the sampled data.
Applying prony algorithm to single-frequency vibration data A2i＝f(ti) And (3) performing operation, and calculating to obtain the single-frequency data characteristic parameters: initial vibration amplitude Im2Frequency f2Attenuation coefficient a2. Further, the analytic expression of the single-frequency vibration signal can be obtained as follows:
Figure BDA0002516205630000071
4) at the k-th operation, discrete data A is collectedi＝f(ti) K-1 single-frequency vibration signals determined k-1 times before stripping
Figure BDA0002516205630000072
Further, intercepting the kth section of single-frequency vibration data on a time window, namely:
Aki＝f(ti) i＝Nk,2,…,Nkend (6)
in the formula, NkTo intercept the start position of the sampled data, NkendTo intercept the end position of the sampled data.
Applying prony algorithm to single-frequency vibration data Aki＝f(ti) And (3) performing operation, and calculating to obtain the single-frequency data characteristic parameters: initial vibration amplitude ImkFrequency fkAttenuation coefficient ak. Further, the analytic expression of the single-frequency vibration signal can be obtained as follows:
Figure BDA0002516205630000073
setting a threshold value gamma, and setting the initial vibration amplitude I of the k-th operationmk<Γ, the operation terminates. Otherwise, repeating the calculation process of the step.
5) Assuming that the calculation is terminated after m operations, m characteristic parameter arrays can be obtained, namely: [ I ] ofmk，fk，ak，tNk]And k is 1,2, …, m. The characteristic parameter fk、akPerforming vibration fault mode identification as modal parameters, and respectively assuming that standard characteristic parameters of typical vibration fault modes are Iref、fref、arefDefining the weighted Euclidean distance lambda as:
Figure BDA0002516205630000074
when λ ≦ ε, this typical vibration failure is indicated. k is a radical of1、k2Is a weight coefficient, and ε is an evaluation deviation.
Characteristic parameter ImkAnd IrefRatio of
Figure BDA0002516205630000075
And obtaining two kinds of prompt information of failure and warning as the evaluation index of the vibration signal intensity. Characteristic parameter tNkIndicating the time when the vibration fault occurs this time.
As a preferred embodiment, k1、k2The values are respectively 0.9 and 0.1, the value range of epsilon is 0-0.3, or the value is set according to field experience values.
When R is more than or equal to 0.8str<When 1, sending out a fault signal; when R is more than or equal to 0.2str<A warning signal is sent out when the temperature is 0.5 ℃; when R is more than or equal to 0.5str<At 0.8, a fault or warning signal may be issued based on field experience.
The present invention will be described in detail below with reference to the accompanying drawings and specific embodiments.
Examples
With reference to fig. 1 to 3, a method for extracting a vibration signal characteristic parameter of a high-voltage alternating-current circuit breaker includes the following steps:
1) according to the vibration signal (t) shown in FIG. 1i,Ai) Constructing a discrete function, namely:
Ai＝f(ti) i＝1,2,…,N (1)
in the formula, t is time, A is vibration signal amplitude, and N is the number of sampling points.
2) For the first operation, for the discrete function Ai＝f(ti) Intercepting a first section of single-frequency vibration data on a time window, namely:
A1i＝f(ti) i＝N1,2,…,N1end (2)
in the formula, N1To intercept the start position of the sampled data, N1endTo intercept the end position of the sampled data. The vibration signal A is derived by applying spline fitting as shown in FIG. 2i＝f(ti) Upper and lower envelope s of the peak1、s2Line of mean value
Figure BDA0002516205630000081
And when large fluctuation occurs on the time axis, taking the sampling point corresponding to the moment as an end position.
Applying prony algorithm to single-frequency vibration data A1i＝f(ti) And (3) performing operation, and calculating to obtain the single-frequency data characteristic parameters: initial vibration amplitude Im1Frequency f1Attenuation coefficient a1. Further, the discrete expression of the single-frequency vibration signal can be obtained as follows:
Figure BDA0002516205630000082
3) from the discrete data A collected during the second operationi＝f(ti) The first single-frequency vibration signal A 'determined by the first operation is stripped'1i. Further, intercepting a second segment of single-frequency vibration data on the time window, namely:
A2i＝f(ti) i＝N2,2,…,N2end (4)
in the formula, N2To intercept the start position of the sampled data, N2endTo intercept the end position of the sampled data.
Applying prony algorithm to single-frequency vibration data A2i＝f(ti) And (3) performing operation, and calculating to obtain the single-frequency data characteristic parameters: initial vibration amplitude Im2Frequency f2Attenuation coefficient a2. Further, the analytic expression of the single-frequency vibration signal can be obtained as follows:
Figure BDA0002516205630000091
4) after 5 times of calculation, 5 vibration modes are obtained after the calculation is terminated, and the characteristic parameters of each mode are shown in the following table:
vibration mode Amplitude (m/s)2) Frequency (Hz) Coefficient of attenuation Moment of oscillation(s)
1 0.15 1000 64.9 0.15
2 0.30 4000 79.9 0.02
3 1.00 6000 70.0 0.025
4 0.50 8000 54.9 0.04
5 0.20 2600 49.6 0.05
6) The characteristic parameter fk、akPerforming vibration fault mode identification as modal parameters, and respectively assuming that standard characteristic parameters of typical vibration fault modes are Iref、fref、arefDefining the weighted Euclidean distance lambda as:
Figure BDA0002516205630000092
when λ ≦ 0.1, this typical vibration failure is indicated. k is a radical of1、k2The values are respectively 0.9 and 0.1, and epsilon is the evaluation deviation.
7) Characteristic parameter ImkAnd IrefRatio of
Figure BDA0002516205630000093
And obtaining two kinds of prompt information of failure and warning as the evaluation index of the vibration signal intensity. When R is more than or equal to 0.8str<When 1, sending out a fault signal; when R is more than or equal to 0.2str<A warning signal is emitted at 0.8.
The technical features of the above embodiments can be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, but should be considered as the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above examples only show some embodiments of the present invention, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the inventive concept, which falls within the scope of the present invention. Therefore, the protection scope of the present patent shall be subject to the appended claims.
It is to be understood that the above description is intended to be illustrative, and not restrictive. Many embodiments and many applications other than the examples provided would be apparent to those of skill in the art upon reading the above description. The scope of the present teachings should, therefore, be determined not with reference to the above description, but should instead be determined with reference to the appended claims, along with the full scope of equivalents to which such claims are entitled. The disclosures of all articles and references, including patent applications and publications, are hereby incorporated by reference for all purposes. The omission in the foregoing claims of any aspect of subject matter that is disclosed herein is not intended to forego such subject matter, nor should the applicant consider that such subject matter is not considered part of the disclosed subject matter.

Claims (6)

1. A method for extracting vibration signal characteristic parameters of a high-voltage alternating-current circuit breaker is characterized by comprising the following steps:
s100, acquiring the number of vibration signals according to a time axisAccording to (t)i,Ai) Constructing a discrete function, namely:
Ai＝f(ti) i＝1,2,…,N (1)
in the formula, t is time, A is vibration signal amplitude, and N is the number of sampling points;
applying the median filter function g to medfilt (y, n) versus the discrete function Ai＝f(ti) Performing noise elimination processing twice, wherein y is discrete data to be filtered, and n is the size of a neighborhood window;
s200, extracting characteristic parameters by applying cubic spline fitting envelope curve to vibration signal data and prony algorithm to be combined to obtain m characteristic parameter arrays [ I ] of the vibration signal datamk，fk，ak，tNk]，k＝1,2,…,n；
S300, assuming that the calculation is terminated after m operations, obtaining m characteristic parameter arrays, namely: [ I ] ofmk，fk，ak，tNk]K is 1,2, …, m; the characteristic parameter fk、akPerforming vibration fault mode identification as modal parameters, and respectively assuming that standard characteristic parameters of typical vibration fault modes are Iref、fref、arefDefining the weighted Euclidean distance lambda as:
Figure FDA0003212932040000011
when lambda is less than or equal to epsilon, the typical vibration fault mode exists; k is a radical of1、k2Is a weight coefficient, and epsilon is an evaluation deviation;
characteristic parameter ImkAnd IrefRatio of
Figure FDA0003212932040000012
Obtaining two kinds of prompt information of failure and warning as the evaluation index of the vibration signal intensity; characteristic parameter tNkShowing the moment when the vibration fault occurs;
step S200 specifically includes the following steps:
s201, in the first operation, the discrete function A after noise elimination is carried outi＝f(ti) Intercepting a first section of single-frequency vibration data on a time window, namely:
A1i＝f(ti) i＝N1,2,…,N1end (2)
in the formula, N1To intercept the start position of the sampled data, N1endThe end position of the sample data is intercepted;
applying prony algorithm to single-frequency vibration data A1i＝f(ti) Calculating to obtain characteristic parameters of the first section of single-frequency vibration data: initial vibration amplitude Im1Frequency f1Attenuation coefficient a1；
And then obtaining a discrete expression of the single-frequency vibration data as follows:
Figure FDA0003212932040000021
s202, in the second operation, the discrete data A is collectedi＝f(ti) The first single-frequency vibration signal A 'determined by the first operation is stripped'1i；
And then, intercepting a second section of single-frequency vibration data on a time window, namely:
A2i＝f(ti) i＝N2,2,…,N2end (4)
in the formula, N2To intercept the start position of the sampled data, N2endIntercepting the end position of the sampled data;
applying prony algorithm to single-frequency vibration data A2i＝f(ti) And (3) calculating to obtain the characteristic parameters of the second section of single-frequency vibration data: initial vibration amplitude Im2Frequency f2Attenuation coefficient a2
And further obtaining an analytical expression of the second section of single-frequency vibration data as follows:
Figure FDA0003212932040000022
s203, during the k-th operation, the discrete data A is collectedi＝f(ti) K-1 single-frequency vibration signals determined k-1 times before stripping
Figure FDA0003212932040000023
And then intercepting the kth section of single-frequency vibration data on a time window, namely:
Aki＝f(ti) i＝Nk,2,…,Nkend (6)
in the formula, NkTo intercept the start position of the sampled data, NkendIntercepting the end position of the sampled data;
applying prony algorithm to single-frequency vibration data Aki＝f(ti) And (3) performing operation, and calculating to obtain characteristic parameters of k-1 single-frequency vibration data: initial vibration amplitude ImkFrequency fkAttenuation coefficient ak(ii) a The analytical expressions of k-1 single-frequency vibration data are obtained as follows:
Figure FDA0003212932040000031
setting a threshold value gamma, and setting the initial vibration amplitude I of the k-th operationmkIf < gamma, the operation is terminated; otherwise, the calculation process of step S203 is repeated.
2. The method for extracting the vibration signal characteristic parameters of the high-voltage alternating-current circuit breaker according to claim 1, wherein the method comprises the following steps: in step S100, the vibration signal acquisition device is composed of an acceleration sensor, an acquisition card, and an upper computer software system.
3. The method for extracting the vibration signal characteristic parameters of the high-voltage alternating-current circuit breaker according to claim 1, wherein the method comprises the following steps: in S201, N1Is a vibration signal (t)i,Ai) At the starting position ofObtaining a vibration signal A by a cubic spline fitting algorithmi＝f(ti) Upper and lower envelope s of the peak1、s2Line of mean value
Figure FDA0003212932040000032
When large fluctuation occurs on the time axis, the sampling point N corresponding to the moment is taken1endTo intercept the end position of the sampled data.
4. The method for extracting the vibration signal characteristic parameters of the high-voltage alternating-current circuit breaker according to claim 1, wherein the method comprises the following steps: in S203, k1、k2The values are respectively 0.9 and 0.1, the value range of epsilon is 0-0.3, or the value is set according to field experience values.
5. The method for extracting the vibration signal characteristic parameters of the high-voltage alternating-current circuit breaker according to claim 1, wherein the method comprises the following steps: when R is more than or equal to 0.8strIf < 1, sending out a fault signal.
6. The method for extracting the vibration signal characteristic parameters of the high-voltage alternating-current circuit breaker according to claim 5, wherein the method comprises the following steps: when R is more than or equal to 0.2strIf the value is less than 0.5, a warning signal is sent out; when R is more than or equal to 0.5strIf the signal is less than 0.8, a fault or warning signal can be sent out according to field experience.
CN202010477132.9A 2020-05-29 2020-05-29 Method for extracting vibration signal characteristic parameters of high-voltage alternating-current circuit breaker Active CN111473860B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202010477132.9A CN111473860B (en) 2020-05-29 2020-05-29 Method for extracting vibration signal characteristic parameters of high-voltage alternating-current circuit breaker

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202010477132.9A CN111473860B (en) 2020-05-29 2020-05-29 Method for extracting vibration signal characteristic parameters of high-voltage alternating-current circuit breaker

Publications (2)

Publication Number Publication Date
CN111473860A CN111473860A (en) 2020-07-31
CN111473860B true CN111473860B (en) 2021-09-24

Family

ID=71763735

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202010477132.9A Active CN111473860B (en) 2020-05-29 2020-05-29 Method for extracting vibration signal characteristic parameters of high-voltage alternating-current circuit breaker

Country Status (1)

Country Link
CN (1) CN111473860B (en)

Families Citing this family (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112504435B (en) * 2020-11-23 2022-09-20 长春工程学院 High-voltage circuit breaker base screw loosening fault detection method

Family Cites Families (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
DE10162118A1 (en) * 2001-12-12 2003-07-17 Siemens Ag Procedure for determining a future voltage and / or current curve
FR2853466B1 (en) * 2003-04-02 2005-05-06 Alstom METHOD FOR DETERMINING THE CLOSURE TIME OF A CIRCUIT BREAKER ON A HIGH VOLTAGE LINE
CN102967800B (en) * 2012-12-10 2015-06-03 辽宁省电力有限公司沈阳供电公司 Method and device for positioning single-phase ground fault section of power distribution network based on transient signal prony algorithm
CN103207351B (en) * 2013-03-12 2015-09-30 西安工程大学 A kind of power transmission line fault locating method based on reclosing
CN103336243B (en) * 2013-07-01 2016-02-10 东南大学 Based on the circuit breaker failure diagnostic method of divide-shut brake coil current signal
US9496111B1 (en) * 2015-09-30 2016-11-15 Siemens Industry, Inc. Prong-less neutral connector assemblies, circuit breakers including prong-less neutral connector, panel boards with flexible neutral bars, and neutral connection methods
CN105528741B (en) * 2016-01-11 2017-03-22 广东电网有限责任公司电力科学研究院 Circuit breaker state identification method based on multi-signal feature fusion
CN105891707A (en) * 2016-05-05 2016-08-24 河北工业大学 Opening-closing fault diagnosis method for air circuit breaker based on vibration signals
CN107732940B (en) * 2017-10-19 2021-11-02 广西电网有限责任公司电力科学研究院 Power system stabilizer parameter optimization test method based on ADPSS

Also Published As

Publication number Publication date
CN111473860A (en) 2020-07-31

Similar Documents

Publication Publication Date Title
EP1094323B1 (en) Method and system for identifying cause of partial discharge
Tang et al. A correlated empirical mode decomposition method for partial discharge signal denoising
CN112362987B (en) Lightning arrester fault diagnosis method based on robust estimation
CN104156568A (en) Transformer fault diagnosis method on basis of weighted gray correlation and fuzzy clustering
JP2005061901A (en) Insulation diagnostic method for electric equipment
CN112213640B (en) Motor fault diagnosis method and related equipment thereof
CN111473860B (en) Method for extracting vibration signal characteristic parameters of high-voltage alternating-current circuit breaker
CN109029959A (en) Method for detecting mechanical state of transformer winding
JP7041896B2 (en) Rotating machine winding insulation deterioration diagnostic device
CN107422232A (en) A kind of digital live detection instrument of power distribution network terminal equipment
CN116520068B (en) Diagnostic method, device, equipment and storage medium for electric power data
CN112285494A (en) Power cable partial discharge mode recognition analysis system
CN112505500A (en) Partial discharge severity evaluation method based on ultrahigh frequency detection mode
CN114034973B (en) Fault area identification method, device and system for distribution line ground fault
EP4001936B1 (en) Automatic partial discharge and noise signals separation using arithmetic coding in time domain and magnitude distributions in frequency domain
KR100645113B1 (en) Noise reduction and Quantity decision method in partial discharge measurement
CN116953435A (en) Cable instantaneous discharge anomaly identification method based on wave recording file
CN108919041B (en) Transformer winding state online monitoring method based on cluster analysis
CN116865672A (en) Photovoltaic module abnormality recognition method and device and computer readable storage medium
CN116184060A (en) Abnormal monitoring method and system suitable for porcelain insulator live working
CN113408383A (en) Audible noise invalid data judgment method based on bounded beta (g, h) distribution and MWKPCA
CN111896871A (en) Motor operation fault analysis system
KR20220166319A (en) motor diagnostics
CN112433148B (en) Method for extracting action time of circuit breaker mechanism from vibration signal
CN114609485B (en) Test system for aging condition of crosslinked polyethylene insulated power cable of new energy automobile

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