CN110320467A - A kind of low-voltage direct circuit breaker failure diagnostic method - Google Patents
A kind of low-voltage direct circuit breaker failure diagnostic method Download PDFInfo
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- CN110320467A CN110320467A CN201910530188.3A CN201910530188A CN110320467A CN 110320467 A CN110320467 A CN 110320467A CN 201910530188 A CN201910530188 A CN 201910530188A CN 110320467 A CN110320467 A CN 110320467A
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- vibration signal
- voltage direct
- circuit breaker
- direct circuit
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- 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
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- 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
- G01R31/3277—Testing of circuit interrupters, switches or circuit-breakers of low voltage devices, e.g. domestic or industrial devices, such as motor protections, relays, rotation switches
Abstract
The invention discloses a kind of low-voltage direct circuit breaker failure diagnostic methods, vibration signal of the low-voltage direct breaker in divide-shut brake movement is obtained by sensor technology, the normalized energy value of each frequency range of vibration signal is extracted as characteristic quantity to vibration signal filtering, removal trend term, denoising, wavelet packet by signal processing technology, the intelligent diagnostics of dc circuit breaker state are finally realized using the neural network Elman with LOCAL FEEDBACK, and the state and remaining life of dc circuit breaker are predicted on longitudinal time；Since Elman neural network has LOCAL FEEDBACK part, this algorithm not only realizes that fault identification is also able to achieve status predication, therefore the method for the present invention provides foundation for the maintenance of dc circuit breaker.
Description
Technical field
The invention belongs to direct current power system electrical equipment status monitoring and diagnostic fields, and in particular to a kind of low-voltage direct
The condition monitoring and fault diagnosis method of breaker.
Background technique
DC distribution has many advantages, such as that transmission line capability is big, power supply reliability is high, power quality is good, mesh compared to AC distribution
The fields such as preceding space flight, ship, rail traffic, new energy distributed power generation have used DC distribution technology.Low-voltage direct is disconnected
Switchgear of the road device as most critical in direct-flow distribution system plays the control action of disjunction closed system and guarantees power distribution network
The protective effect of safe and reliable operation, therefore the state of dc circuit breaker directly affects the stable operation of direct-flow distribution system.
For low-voltage direct breaker as a kind of electrical equipment, mechanical part is more, is difficult directly to observe breaker from the appearance
State so that go assessment breaker whether to occur deteriorating or failure.Counted through domestic and international associated mechanisms, find breaker 70% with
Upper failure is mechanical breakdown.And pervious correlative study concentrates on the monitoring and diagnosis of high-voltage circuitbreaker substantially.
Therefore a kind of method that can be directed to distribution side low-voltage direct breaker on-line monitoring and fault diagonosing, assessment are needed
The state of low-voltage direct breaker realizes the fault diagnosis and prospective maintenance of low-voltage direct breaker.This is for improving low pressure
The reliability of dc circuit breaker and the lifecycle management for realizing predictive maintenance strategy are significantly.
Summary of the invention
In view of the deficiencies of the prior art, a kind of according to low-voltage direct Circuit breaker vibration signal it is an object of the invention to propose
The method for carrying out fault diagnosis.
The technical solution adopted by the present invention to solve the technical problems is: a kind of low-voltage direct circuit breaker failure diagnosis side
Method selects vibrating sensor, the vibration signal that measurement low-voltage direct breaker generates during divide-shut brake, by suitably shaking
Dynamic Signal Pretreatment and processing method extract vibr ation signals, will be input to after characteristic quantity dimension-reduction treatment by test sample
Having in LOCAL FEEDBACK functional neurosurgery network Elman after training, realize to the fault diagnosis of low-voltage direct circuit-breaker status and
Prediction.
It is further to realize that step includes:
(1), proper vibration sensor is selected, the vibration signal during low-voltage direct circuit-breaker switching on-off is acquiredx(n), pass through
Communication board is transferred to host computer；
(2), by signalx(n) by design low-pass filtering treatment obtain signalx 1(n)；
(3), signal is handled using the method for least square method polynomial fittingx 1(n), the vibration letter after obtaining removal trend term
Numberx 2(n)；
(4), using heuristic wavelet soft-threshold denoising method, to signalx 2(n) signal is obtained after Wavelet Denoising Methodx 3(n)；
(5), signal is handled in full frequency band using two layers of wavelet packet decomposition algorithmx 3(n), wavelet basis function db10, at normalization
Reason obtains the energy accounting of each frequency rangeT=[t 1 t 2 t 3 t 4]；
It (6), will according to ranges of sensors and low pass filter cutoff frequency fcTDimension-reduction treatment obtainsT j ；
(7), willT j It is input to Elman neural network and carries out Fault Pattern Recognition, diagnose low-voltage direct circuit breaker failure, must pay a home visit
Disconnected result: otherwise the shutdown maintenance if failure or serious deterioration continues to acquire vibration signal；
(8), (2)~(6) step is repeated in the case where not obtaining halt instruction, otherwise state out of service.
Further,
The sensor requirements of step (1) selection information in the full time domain scale of vibration signal does not lack, and meets range item
Part, and high sensitivity, measurement frequency range are wide.
The filter specifications of step (2) selection can effectively remove the interference of original vibration signal, avoid selecting at random
Take the blindness of cutoff frequency.
The step (4) denoises vibration signal using wavelet soft-threshold denoising method, and the selection of threshold value is using heuristic
Threshold estimation method, to realize the optimal estimation of threshold value.
The step (5) extracts vibr ation signals using wavelet packet decomposition algorithm, in full frequency band to vibration signal
Analysis.
The effective range of signal is influenced by cutoff frequency in the step (6), and removal is cut higher than low-pass filter
The only characteristic quantity of frequency signal leaves effective feature volume, reduces characteristic quantity dimension.Wherein, to through removal trend term and heuristic
Wavelet soft-threshold denoising method obtains pretreated vibration signal, respectively obtains four frequency bands by two layers of WAVELET PACKET DECOMPOSITION
Energy accounts for the value of gross energyT=[t 1 t 2 t 3 t 4], by the cutoff frequency of sensor frequency range and low-pass filter, remove high band
Two characteristic quantitiesT j =[t 1 t 2], realize characteristic quantity dimension-reduction treatment.
The acquisition methods of training sample in the step (7) are as follows: (71), measure vibration when simulated failure under test conditions
Dynamic signal, simulated failure such as divide-shut brake coil is under-voltage or over-voltage, divide-shut brake coil clamping stagnation, divide-shut brake coil aging；(72), exist
Divide-shut brake vibration signal is measured in life test, which reflects the breaker vertical feature changed of axis at any time；(73), in reality
Divide-shut brake vibration signal is measured in operating condition.
Wherein, normal combined floodgate, normal separating brake are measured, closing coil is under-voltage to 180V, closing coil is under-voltage to 160V, combined floodgate
Vibration signal under six kinds of analog cases such as coil aging, closing coil clamping stagnation tests multi-group data to the case where every kind of simulation,
The vibration signal in breaker life test and in actual motion is measured, extracts effective feature volume using above-mentioned steps, and random
It is divided into training sample and test sample, training sample is inputted into Elman neural network learning, after test sample input study
Neural network obtains diagnostic result.
The beneficial effects of the present invention are: obtaining vibration of the low-voltage direct breaker in divide-shut brake movement by sensor technology
Dynamic signal extracts each frequency of vibration signal to vibration signal filtering, removal trend term, denoising, wavelet packet by signal processing technology
The normalized energy value of section finally realizes dc circuit breaker using the neural network Elman with LOCAL FEEDBACK as characteristic quantity
The intelligent diagnostics of state, and predict on longitudinal time the state and remaining life of dc circuit breaker, it is dc circuit breaker
Maintenance provides foundation.
Detailed description of the invention
Fig. 1 is the flow chart of the method for the present invention；
Fig. 2 is structural block diagram of the invention；
Fig. 3 is vibration signal after low-pass filtering；
Fig. 4 is vibration signal FFT spectrum after filtering；
Fig. 5 is that the normalized energy of vibration signal when extracting primary normal combined floodgate using two layers of wavelet packet decomposition algorithm composes histogram
Figure；
Fig. 6 is the Elman neural network block diagram used.
Specific embodiment
The present invention is described in further detail below in conjunction with the accompanying drawings.
Low-voltage direct breaker is the key equipment in direct current power supply-distribution system, in metro traction DC power-supply system
Dc circuit breaker.Frequent divide-shut brake control primary circuit on-off is needed in routine work, frequent divide-shut brake influences whether low pressure
The state of dc circuit breaker and service life.Periodic maintenance when tradition checks the method for circuit-breaker status exists and owes maintenance and cross to safeguard
The problem of.
The present invention devises a kind of method for carrying out fault diagnosis according to low-voltage direct Circuit breaker vibration signal, in the method
Under may be implemented:
Select a kind of range meet, highly sensitive, wide frequency ranges vibrating sensor, measurement obtains low-voltage direct breaker
The vibration signal that information in divide-shut brake movement does not lack.
A kind of low-pass filter is designed, the interference of original vibration signal is effectively removed, avoids and randomly select cutoff frequency
The blindness of rate.
The cutoff frequency fc of low-pass filtering choose be considered as 2 points: 1 be vibrating sensor frequency range f, 2 be to vibration
The analysis of signal FFT spectrum；The cutoff frequency fc for determining low-pass filter according to 1,2.
After the feature vector that wavelet packet is handled, by dimension-reduction treatment, effective feature volume is further obtained.
The acquisition methods of training sample: 1 be under test conditions simulated failure when measure vibration signal, simulated failure as point
Closing coil is under-voltage or over-voltage, divide-shut brake coil clamping stagnation, divide-shut brake coil aging；2 be that divide-shut brake is measured in life test
Vibration signal, the data reflect the breaker vertical feature changed of axis at any time, and 3 be that divide-shut brake vibration letter is measured in actual condition
Number.
Training sample includes simulated failure data, actual operating data and the testing data of life-span that may occur, these
Sample data, which can be used not only for Fault Pattern Recognition, can be also used for failure predication.
Elman neural network is a kind of local regression network, can remember past state, realizes status predication, the net
Network applies to circuit breaker failure diagnostic system, and circuit breaker failure pattern-recognition not only may be implemented, and can also realize status predication,
To not break down still, the status predication of serious deterioration has come out, and the hard time maintenance of low-voltage direct breaker is changed into pre-
The property surveyed maintenance.
In conjunction with attached drawing 1, the object of the present invention is achieved like this:
(1) proper vibration sensor is selected, the vibration signal during low-voltage direct circuit-breaker switching on-off is acquiredx(n), by logical
News plate is transferred to host computer.
(2) willx(n) by design low-pass filtering treatment obtain signalx 1(n)。
(3) it is handled using the method for least square method polynomial fittingx 1(n) obtain the vibration signal after removal trend termx 2
(n)。
(4) signal is obtained using heuristic wavelet soft-threshold denoising methodx 3(n)。
(5) using two layers of wavelet packet decomposition algorithm in full frequency band processingx 3(n), wavelet basis function db10, at normalization
Reason obtains the energy accounting of each frequency rangeT=[t 1 t 2 t 3 t 4]。
It (6) will according to ranges of sensors and low pass filter cutoff frequency fcTDimension-reduction treatment obtainsT j 。
(6) willT j It is input to Elman neural network and carries out Fault Pattern Recognition, diagnose low-voltage direct circuit breaker failure.If
It breaks down or serious deterioration then shutdown maintenance, otherwise continues to acquire vibration signal.
(7) (2)~(6) step is repeated in the case where not obtaining halt instruction, otherwise state out of service.
By comparing selecting a kind of vibrating sensor for meeting range, high sensitivity, wide frequency ranges, model
ADXL1004, range are ± 500g, and sensitivity 10mV/g, frequency range is that 1~24000Hz measures low pressure vibrating sensor.
It analyzes vibration signal FFT spectrum and combines ADXL1004 sensor frequency range, devise the low pass of fc=30kHz
Filter filters original vibration signal.
Pretreated vibration signal is obtained through removal trend term and heuristic wavelet soft-threshold denoising method again, by two layers
The energy that WAVELET PACKET DECOMPOSITION respectively obtains four frequency bands accounts for the value of gross energyT=[t 1 t 2 t 3 t 4], by sensor frequency range and
The cutoff frequency of low-pass filter removes two characteristic quantities of high bandT j =[t 1 t 2], realize characteristic quantity dimension-reduction treatment.
Measure vibration signal under six kinds of analog cases: 1 normal combined floodgate, 2 normal separating brakes, 3 closing coils are under-voltage to 180V, 4
Closing coil is under-voltage to arrive 160V, 5 closing coil agings (6 Ω resistance of closing coil series connection), 6 closing coil clamping stagnations.To every kind of mould
Quasi- situation tests multi-group data, the vibration signal in breaker life test and in actual motion is measured, using above-mentioned steps
Effective feature volume is extracted, and is randomly divided into training sample and test sample, training sample is inputted into Elman neural network learning,
Neural network after test sample input study is obtained into diagnostic result.
Figure it is seen that small have of vibrating sensor range outranges phenomenon, sensitivity is low and leads to the signal of acquisition
There is loss of learning.
Fig. 3 and Fig. 4 is vibration signal waveforms and fast Fourier (FFT) frequency spectrum behind filtering front and back.Comparison diagram 3 and Fig. 4 can
To find out after low-pass filtering, vibration signal high-frequency noise reduces very much.
Two layers of WAVELET PACKET DECOMPOSITION normalized energy composes histogram when Fig. 5 is normal closes a floodgate；From Fig. 3 and Fig. 4 it can be seen that
High band energy accounting is few, needs using characteristic quantity dimension-reduction treatment.
Fig. 6 is the Elman neural network block diagram used, mainly includes input layer, output layer, hidden layer.Its feature exists
In also LOCAL FEEDBACK, has the function of time prediction.This is indulged from time shaft for the diagnosis of low-voltage direct circuit breaker failure
To vibr ation signals are compared, so that prediction low-voltage direct circuit breaker failure pattern-recognition and status predication are realized, into one
Step realizes low-voltage direct breaker lifecycle management.
The above-described embodiments merely illustrate the principles and effects of the present invention, and the embodiment that part uses, for
For those skilled in the art, without departing from the concept of the premise of the invention, can also make it is several deformation and
It improves, these are all within the scope of protection of the present invention.
Claims (10)
1. a kind of low-voltage direct circuit breaker failure diagnostic method, it is characterised in that: selection vibrating sensor, measurement low-voltage direct are disconnected
The vibration signal that road device generates during divide-shut brake extracts vibr ation signals, will be input to after characteristic quantity dimension-reduction treatment
Having in LOCAL FEEDBACK functional neurosurgery network Elman after test sample training, realizes to low-voltage direct circuit-breaker status
Fault diagnosis and prediction.
2. a kind of low-voltage direct circuit breaker failure diagnostic method according to claim 1, which is characterized in that realize step packet
It includes:
(1), proper vibration sensor is selected, the vibration signal during low-voltage direct circuit-breaker switching on-off is acquiredx(n), pass through
Communication board is transferred to host computer；
(2), by signalx(n) signal obtained by low-pass filtering treatmentx 1(n)；
(3), signal is handled using the method for least square method polynomial fittingx 1(n), the vibration signal after obtaining removal trend termx 2(n)；
(4), using heuristic wavelet soft-threshold denoising method, to signalx 2(n) signal is obtained after Wavelet Denoising Methodx 3(n)；
(5), signal is handled in full frequency band using two layers of wavelet packet decomposition algorithmx 3(n), wavelet basis function db10, at normalization
Reason obtains the energy accounting of each frequency rangeT=[t 1 t 2 t 3 t 4]；
It (6), will according to ranges of sensors and low pass filter cutoff frequency fcTDimension-reduction treatment obtainsT j ；
(7), willT j It is input to Elman neural network and carries out Fault Pattern Recognition, diagnose low-voltage direct circuit breaker failure, must pay a home visit
Disconnected result: otherwise the shutdown maintenance if failure or serious deterioration continues to acquire vibration signal；
(8), (2)~(6) step is repeated in the case where not obtaining halt instruction, otherwise state out of service.
3. a kind of low-voltage direct circuit breaker failure diagnostic method according to claim 2, which is characterized in that the step
(1) sensor requirements selected information in the full time domain scale of vibration signal does not lack, and meets range condition, and highly sensitive
Degree, measurement frequency range are wide.
4. a kind of low-voltage direct circuit breaker failure diagnostic method according to claim 2, which is characterized in that the step
(2) filter specifications selected can effectively remove the interference of original vibration signal, avoid the blindness for randomly selecting cutoff frequency.
5. a kind of low-voltage direct circuit breaker failure diagnostic method according to claim 2, which is characterized in that the step
(4) vibration signal is denoised using wavelet soft-threshold denoising method, the selection of threshold value uses heuristic threshold estimation method, to realize threshold
The optimal estimation of value.
6. a kind of low-voltage direct circuit breaker failure diagnostic method according to claim 2, which is characterized in that the step
(5) vibr ation signals are extracted using wavelet packet decomposition algorithm, in full frequency band to analysis of vibration signal.
7. a kind of low-voltage direct circuit breaker failure diagnostic method according to claim 2, which is characterized in that the step
(6) removal is higher than the characteristic quantity of low pass filter cutoff frequency signal in, leaves effective feature volume, reduces characteristic quantity dimension.
8. a kind of low-voltage direct circuit breaker failure diagnostic method according to claim 7, which is characterized in that the step
(6) pretreated vibration signal is obtained to through removal trend term and heuristic wavelet soft-threshold denoising method in, it is small by two layers
The energy that wave packet respectively obtains four frequency bands accounts for the value of gross energyT=[t 1 t 2 t 3 t 4], by sensor frequency range and low
The cutoff frequency of bandpass filter removes two characteristic quantities of high bandT j =[t 1 t 2], realize characteristic quantity dimension-reduction treatment.
9. a kind of low-voltage direct circuit breaker failure diagnostic method according to claim 2, which is characterized in that the step
(7) acquisition methods of training sample in are as follows:
(71), under test conditions simulated failure when measure vibration signal, simulated failure such as divide-shut brake coil is under-voltage or over-voltage,
Divide-shut brake coil clamping stagnation, divide-shut brake coil aging；
(72), divide-shut brake vibration signal is measured in life test, which reflects the breaker vertical feature changed of axis at any time；
(73), divide-shut brake vibration signal is measured in actual condition.
10. a kind of low-voltage direct circuit breaker failure diagnostic method according to claim 9, which is characterized in that the step
Suddenly normal combined floodgates, normal separating brake are measured in (7), closing coil is under-voltage to 180V, closing coil is under-voltage to 160V, closing coil is old
Vibration signal under six kinds of analog cases such as change, closing coil clamping stagnation tests multi-group data to the case where every kind of simulation, and measurement is disconnected
Vibration signal in the device life test of road and in actual motion extracts effective feature volume using above-mentioned steps, and is randomly divided into instruction
Practice sample and test sample, training sample is inputted into Elman neural network learning, by the nerve net after test sample input study
Network obtains diagnostic result.
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| CN111289800A (en) * | 2020-03-05 | 2020-06-16 | 国网安徽省电力有限公司 | Small-resistance vibration monitoring method based on generalized regression neural network |
| CN111781494A (en) * | 2020-07-09 | 2020-10-16 | 西安交通大学 | Improved automatic on-line detection method and device for mechanical characteristics of circuit breaker |
| CN112345213A (en) * | 2020-09-18 | 2021-02-09 | 华能河南中原燃气发电有限公司 | Low-voltage direct-current circuit breaker mechanical fault diagnosis method |
| CN112947374A (en) * | 2021-02-09 | 2021-06-11 | 上海海事大学 | Intelligent self-healing control method for electric propulsion of regional distribution ship |
| CN113466679A (en) * | 2021-05-17 | 2021-10-01 | 浙江工业大学 | Method for estimating service life of circuit breaker |
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| CN110926778A (en) * | 2019-11-29 | 2020-03-27 | 国网天津市电力公司电力科学研究院 | Mechanical fault diagnosis method for gas insulated switchgear assembly based on abnormal vibration |
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| CN111781494A (en) * | 2020-07-09 | 2020-10-16 | 西安交通大学 | Improved automatic on-line detection method and device for mechanical characteristics of circuit breaker |
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| CN112947374A (en) * | 2021-02-09 | 2021-06-11 | 上海海事大学 | Intelligent self-healing control method for electric propulsion of regional distribution ship |
| CN113466679A (en) * | 2021-05-17 | 2021-10-01 | 浙江工业大学 | Method for estimating service life of circuit breaker |
| CN113466679B (en) * | 2021-05-17 | 2024-01-05 | 浙江工业大学 | Circuit breaker service life estimation method |
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