CN108645507A - A kind of high-adaptability GIL vibration online monitoring neural network devices - Google Patents
A kind of high-adaptability GIL vibration online monitoring neural network devices Download PDFInfo
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- CN108645507A CN108645507A CN201810338509.5A CN201810338509A CN108645507A CN 108645507 A CN108645507 A CN 108645507A CN 201810338509 A CN201810338509 A CN 201810338509A CN 108645507 A CN108645507 A CN 108645507A
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- 238000013528 artificial neural network Methods 0.000 title claims abstract description 27
- 238000012544 monitoring process Methods 0.000 title claims abstract description 22
- 238000001914 filtration Methods 0.000 claims abstract description 7
- 230000006855 networking Effects 0.000 claims abstract description 7
- 238000004458 analytical method Methods 0.000 claims abstract description 4
- HBBGRARXTFLTSG-UHFFFAOYSA-N Lithium ion Chemical compound [Li+] HBBGRARXTFLTSG-UHFFFAOYSA-N 0.000 claims description 9
- 229910001416 lithium ion Inorganic materials 0.000 claims description 9
- 238000004891 communication Methods 0.000 claims description 8
- 239000012212 insulator Substances 0.000 claims description 6
- 210000002569 neuron Anatomy 0.000 claims description 6
- 238000009434 installation Methods 0.000 claims description 4
- 230000009466 transformation Effects 0.000 claims description 4
- 230000001133 acceleration Effects 0.000 claims description 3
- 230000005540 biological transmission Effects 0.000 claims description 3
- 210000004027 cell Anatomy 0.000 claims description 3
- 239000000284 extract Substances 0.000 claims description 2
- 230000035945 sensitivity Effects 0.000 abstract description 4
- 230000007257 malfunction Effects 0.000 abstract description 3
- 238000000034 method Methods 0.000 description 4
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- 230000003321 amplification Effects 0.000 description 2
- 238000010276 construction Methods 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 239000007789 gas Substances 0.000 description 2
- 238000009413 insulation Methods 0.000 description 2
- 238000003199 nucleic acid amplification method Methods 0.000 description 2
- 238000012360 testing method Methods 0.000 description 2
- WHXSMMKQMYFTQS-UHFFFAOYSA-N Lithium Chemical compound [Li] WHXSMMKQMYFTQS-UHFFFAOYSA-N 0.000 description 1
- 230000003044 adaptive effect Effects 0.000 description 1
- 238000013473 artificial intelligence Methods 0.000 description 1
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- 238000009826 distribution Methods 0.000 description 1
- 230000005684 electric field Effects 0.000 description 1
- 230000005672 electromagnetic field Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000000605 extraction Methods 0.000 description 1
- 238000009940 knitting Methods 0.000 description 1
- 229910052744 lithium Inorganic materials 0.000 description 1
- 230000007774 longterm Effects 0.000 description 1
- 238000012423 maintenance Methods 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 229910052751 metal Inorganic materials 0.000 description 1
- 239000002184 metal Substances 0.000 description 1
- 239000000203 mixture Substances 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
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- 210000005036 nerve Anatomy 0.000 description 1
- 210000000653 nervous system Anatomy 0.000 description 1
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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
- G01R31/12—Testing dielectric strength or breakdown voltage ; Testing or monitoring effectiveness or level of insulation, e.g. of a cable or of an apparatus, for example using partial discharge measurements; Electrostatic testing
- G01R31/1227—Testing dielectric strength or breakdown voltage ; Testing or monitoring effectiveness or level of insulation, e.g. of a cable or of an apparatus, for example using partial discharge measurements; Electrostatic testing of components, parts or materials
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/06—Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons
- G06N3/061—Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons using biological neurons, e.g. biological neurons connected to an integrated circuit
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Abstract
A kind of high-adaptability GIL vibration online monitoring neural network devices of the disclosure, including vibrating sensor, data collector, radio network gateway and terminal computer；Wherein；Vibrating sensor be fixedly mounted in a manner of neural network networking the surfaces GIL and with GIL intimate surface contacts, to detect GIL vibrational waveform and by vibrational waveform be converted into voltage signal output；Data collector is connected with vibrating sensor, for receive vibrating sensor output voltage signal and be amplified filtering after using ADC be converted into digital signal output；Radio network gateway is used to receive the digital signal of data collector output and by the digital signal terminal computer, and terminal computer obtains the fault condition of GIL according to the Digital Signal Analysis.The present apparatus has very strong adaptability and high sensitivity, to realize comprehensively accurately malfunction monitoring to GIL, ensure that the safety of power grid.
Description
Technical field
The present invention relates to electrical equipment information collecting devices, and in particular to a kind of high-adaptability GIL vibration online monitorings god
Through network equipment.
Background technology
GIL is a kind of using SF6 gases or the metal enclosed transmission facility of SF6 and N2 Mixed gas insulations, is had defeated
Capacitance is big, spatial distribution is flexible, reliability is high and environment influences small feature, therefore extremely agrees with modern city grid generation
Capacity is continuously increased the needs being transformed with power grid construction.However the manufacturing process of GIL is various, requires maintenance craft very smart
Carefully, careless slightly to form correlated quality problem；And it once breaks down and will result in extremely serious consequence.
In current GIL monitoring tests, vibration monitoring is very effective GIL malfunction monitoring means.According to what is detected
Vibration waveform signal judges whether GIL insulation fault occurs, can find accident and the solution of GIL equipment in time, this for
The further genralrlizations of GIL in the power system, there is significant role.But the current acquisition method to vibration signal lacks height
Adaptability can realize that the neural network of GIL vibration online monitorings is lacked at present.The construction method of neural network has been applied
In many fields, but the particularity due to GIL apparatus structures and the high request to adaptability, it is difficult to indiscriminately imitate existing method.
Invention content
It is an object of the invention to overcome above-mentioned the deficiencies in the prior art, a kind of online prison of high-adaptability GIL vibrations is provided
Neural network device is surveyed, effectively to be monitored to GIL.
To achieve the above object, the technical scheme is that：
A kind of high-adaptability GIL vibration online monitoring neural network devices, including vibrating sensor, data collector, nothing
Gauze closes and terminal computer；Wherein；
The vibrating sensor is fixedly mounted on the surfaces GIL in a manner of neural network networking and is closely connect with the surfaces GIL
It touches, to detect the vibrational waveform of GIL and convert vibrational waveform to voltage signal output；
The data collector is connected with vibrating sensor, and the voltage signal for receiving vibrating sensor output is gone forward side by side
After row amplification filtering digital signal output is converted into using ADC；
The radio network gateway is used to receive the digital signal of data collector output and by the digital signal terminal computer,
Terminal computer obtains the fault condition of GIL according to the Digital Signal Analysis.
The mode of the neural network networking is specially：
GIL pipes mother is segmented according to several nodes, each section is numbered, it is exhausted to three pillars in each section
Edge carries out number consecutively, and three support insulators, three feet are numbered respectively；
In each node installation vibrating sensor and data collector, each node constitutes a neuron, multiple god
It is constituted through member network-like.
The vibrating sensor is connected by shielded cable with data collector.
The data collector includes gain-programmed amplifier, filter, analog-digital converter, microcontroller and channel radio
Interrogate module；
The gain-programmed amplifier meets analog-digital converter for the voltage signal that vibrating sensor exports to be amplified to
Input range；
The filter extracts useful exchange point for extracting the amplified voltage signal of gain-programmed amplifier
It measures and carries out antialiasing low-pass filtering；
The analog-digital converter is used to the analog signal that filter exports being converted into digital signal and export to microcontroller
Device；
The micro controller is sent to wireless communication module after being packaged received digital signal；
The digital signal that micro controller transmission comes is converted to electromagnetic wave and is sent in the air by the wireless communication module.
The data collector further includes lithium ion battery and solar battery power transformation and management module；Lithium-ion electric
Pond is gain amplifier, filter, analog-digital converter, microcontroller and wireless communication module provide operating voltage；Solar energy
Battery supply transformation is connected with management module and lithium ion battery, solar cell is exported electric energy to lithium-ion electric
It charges in pond.
The vibrating sensor using built-in IC piezo-electric type technical grade acceleration transducer.
The filter is bandpass filter.
Compared with prior art, the present invention advantage is：
The method that the high-adaptability GIL vibration online monitoring neural network devices of the present invention use neural network networking,
Node is arranged in GIL pipe mothers according to certain rules everywhere, into row information by way of simulating cerebral nerve network processes, recall info
Processing.One neural unit of each node on behalf, overcome the artificial intelligence of traditional logic-based symbol processing intuition,
Defect in terms of unstructured information has the characteristics that letter is collected in adaptive, self-organizing, the nervous system of neuron network simulation people
Breath, and information is sent to terminal in time and carries out analyzing processing, there is very strong adaptability and high sensitivity, to GIL realities
Now comprehensive accurately malfunction monitoring, ensure that the safety of power grid.
Description of the drawings
Fig. 1 is the structural representation of high-adaptability GIL vibration online monitoring neural network devices provided in an embodiment of the present invention
Figure；
Fig. 2 is the schematic diagram of neural network；
Fig. 3 is the signal transmission block diagram of radio network gateway.
Specific implementation mode
Present disclosure is described in further details with reference to the accompanying drawings and detailed description.
Embodiment：
Refering to fig. 1 shown in -3, high-adaptability GIL vibration online monitoring neural network devices provided in this embodiment can
It realizes the vibrational waveform of long-term online acquisition GIL and is uploaded to data terminal, including be fixedly mounted on surfaces GIL and close therewith
The vibrating sensor of contact, the data collector being connect by shielded cable with vibrating sensor, the data collector pass through nothing
Line mode connects radio network gateway, is connected to terminal computer via radio network gateway, terminal computer is analyzed and obtained to data
Go out to obtain the fault condition of GIL.
Wherein, which is fixedly mounted on surfaces GIL and close with the surfaces GIL in a manner of neural network networking
Contact, the mode of neural network networking are specially：
Step 1：GIL pipes mother is segmented by certain node, 1,2,3 ... are numbered to each section, to each section
Three interior support insulator number consecutivelies are 1,2,3 ..., and number A, B, C, A1-2 are three feet of support insulator three respectively
The foot A of second three support insulator of first segment.
Step 2：In each node installation signal picker and sensor, each node constitutes a neuron, each
Neuron composition on node is network-like, such as Fig. 2.Artificial neural network system has very high fault-tolerance, robustness and from group
Knitting property, even if connecting line is destroyed by very high level, it remains to be in Optimization Work shape.
Step 3：Neural network output end output signal, terminal is sent to through wireless module.
In addition, in order to ensure that the accuracy of monitoring result, vibrating sensor should have following features：(1) sensor should
The wide frequency bandwidth of high sensitivity, noise low (2).Frequency bandwidth is wider, including information content it is bigger, then detectable failure classes
Type and range are wider.(3) there is good effectiveness.In view of equipment under test is that heavy current installation and scene may also be deposited
In other high pressures or high-current equipment, these equipment can be by the work of electric field and magnetic interference sensor, therefore sensor is answered
With good interference protection measure.(4) sensor output impedance is low.Low output impedance can avoid sensor output signal and connect
It is interfered in line.(5) small, light-weight.Light sensor weight is conducive to mitigate load effect.(6) operating temperature range
Wide, dust-proof, rain-proof.According to above-mentioned specific requirement, and in view of the technical merit of current vibrating sensor, the present embodiment shakes
Dynamic sensor adopts the piezo-electric type technical grade acceleration transducer of built-in IC, and the key technical indexes is：
Sensitivity：500mV/g
3dB frequencies：20Hz~15kHz
Operating temperature range：- 40~80 DEG C
Weight：50 grams of ＜
Built-in IC amplifications, low input impedance；Double-layer shielding structure can effectively shield low frequency electromagnetic field interference and high-frequency electromagnetic
Wave interference.
Specifically, above-mentioned data collector includes gain-programmed amplifier, filter, high-speed ADC, MCU (microcontrollers
Device), wireless communication module, lithium ion battery, solar battery power transformation and management module.
Gain-programmed amplifier (PGA), gain can be selected by microcontroller (MCU), and the output of vibrating sensor is believed
Number suitable size is amplified to match the input range of ADC.Filter is bandpass filter, and effect is the useful exchange of extraction
Component simultaneously carries out antialiasing low-pass filtering.The analog signal that filter exports is converted to digital letter by ADC, that is, analog-digital converter
Number.Microcontroller (MCU) sends wireless module to after receiving the data of ADC and packing, MCU be also responsible for coordinating other modules, into
Row protocol analysis etc..The MCU digital signals sent are converted to electromagnetic wave and are sent in the air by wireless module.There is suitable sunlight
When irradiation, charging module is charged using the electric energy that exports of solar cell to lithium battery.DC/DC modules export stable DC
Pressure, operating voltage is provided for amplifier, filter, ADC, microcontroller, wireless module.Signal transmit the specific steps are：
1, the Vibration propagation that the vibration or when normal work that GIL failures generate generate is to the surfaces GIL, by vibrating sensor
It detects, vibrating sensor output voltage signal；
2, the data collector being connected with vibrating sensor receives the voltage signal of vibrating sensor output and is put
After big filtering digital signal is converted into using ADC；
3, digital signal is sent to radio network gateway with internal wireless module.
Gain-programmed amplifier (PDA), gain can be selected by microcontroller (MCU), and the output of vibrating sensor is believed
Number suitable size is amplified to match the input range of ADC.
Wherein, as shown in figure 3, the wireless module can receive or send wireless signal, when it receives wireless signal
Wireless signal is converted into digital signal and passes to MCU (microcontroller), MCU will be passed to RS232 serial ports again.Terminal computer
RS232 rs 232 serial interface signals can directly be received.
Above-described embodiment simply to illustrate that the present invention technical concepts and features, it is in the art the purpose is to be to allow
Those of ordinary skill cans understand the content of the present invention and implement it accordingly, and it is not intended to limit the scope of the present invention.It is all
It is the equivalent changes or modifications made according to the essence of the content of present invention, should all covers within the scope of the present invention.
Claims (7)
1. a kind of high-adaptability GIL vibration online monitoring neural network devices, which is characterized in that including vibrating sensor, data
Collector, radio network gateway and terminal computer；Wherein；
The vibrating sensor be fixedly mounted in a manner of neural network networking the surfaces GIL and with GIL intimate surface contacts, use
To detect the vibrational waveform of GIL and convert vibrational waveform to voltage signal output；
The data collector is connected with vibrating sensor, and the voltage signal for receiving vibrating sensor output is simultaneously put
After big filtering digital signal output is converted into using ADC；
The radio network gateway is used to receive the digital signal of data collector output and by the digital signal terminal computer, terminal
Computer obtains the fault condition of GIL according to the Digital Signal Analysis.
2. high-adaptability GIL vibration online monitoring neural network devices as described in claim 1, which is characterized in that the god
Mode through network organizing is specially：
GIL pipes mother is segmented according to several nodes, each section is numbered, to three support insulators in each section
Number consecutively is carried out, three support insulators, three feet are numbered respectively；
In each node installation vibrating sensor and data collector, each node constitutes a neuron, multiple neurons
It constitutes network-like.
3. high-adaptability GIL vibration online monitoring neural network devices as claimed in claim 1 or 2, which is characterized in that described
Vibrating sensor is connected by shielded cable with data collector.
4. high-adaptability GIL vibration online monitoring neural network devices as claimed in claim 3, which is characterized in that the number
Include gain-programmed amplifier, filter, analog-digital converter, microcontroller and wireless communication module according to collector；
The gain-programmed amplifier meets the defeated of analog-digital converter for the voltage signal that vibrating sensor exports to be amplified to
Enter range；
The filter extracts useful AC compounent simultaneously for extracting the amplified voltage signal of gain-programmed amplifier
Carry out antialiasing low-pass filtering；
The analog-digital converter is used to the analog signal that filter exports being converted into digital signal and export to microcontroller；
The micro controller is sent to wireless communication module after being packaged received digital signal；
The digital signal that micro controller transmission comes is converted to electromagnetic wave and is sent in the air by the wireless communication module.
5. high-adaptability GIL vibration online monitoring neural network devices as claimed in claim 4, which is characterized in that the number
Further include lithium ion battery and solar battery power transformation and management module according to collector；Lithium ion battery amplifies for gain
Device, filter, analog-digital converter, microcontroller and wireless communication module provide operating voltage；Solar battery power converts
It is connected with management module and lithium ion battery, the electric energy that exports of solar cell to charge to lithium ion battery.
6. high-adaptability GIL vibration online monitoring neural network devices as claimed in claim 1 or 2, which is characterized in that described
Vibrating sensor using built-in IC piezo-electric type technical grade acceleration transducer.
7. high-adaptability GIL vibration online monitoring neural network devices as described in claim 4 or 5, which is characterized in that described
Filter is bandpass filter.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111488969A (en) * | 2020-04-03 | 2020-08-04 | 北京思朗科技有限责任公司 | Execution optimization method and device based on neural network accelerator |
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|---|---|---|---|---|
| EP0616291A3 (en) * | 1993-03-18 | 1996-07-10 | Hitachi Ltd | A method of configuring a neural network and a diagnosis/recognition system using the same. |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN111488969B (en) * | 2020-04-03 | 2024-01-19 | 北京集朗半导体科技有限公司 | Execution optimization method and device based on neural network accelerator |
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