CN108645507B - High-adaptability GIL vibration online monitoring neural network device - Google Patents
High-adaptability GIL vibration online monitoring neural network device Download PDFInfo
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- HBBGRARXTFLTSG-UHFFFAOYSA-N Lithium ion Chemical compound [Li+] HBBGRARXTFLTSG-UHFFFAOYSA-N 0.000 claims description 9
- 238000004891 communication Methods 0.000 claims description 9
- 229910001416 lithium ion Inorganic materials 0.000 claims description 9
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- 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
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Abstract
The invention discloses a high-adaptability GIL vibration online monitoring neural network device, which comprises a vibration sensor, a data acquisition unit, a wireless gateway and a terminal computer; wherein; the vibration sensor is fixedly arranged on the surface of the GIL in a neural network networking mode and is in close contact with the surface of the GIL, and is used for detecting the vibration waveform of the GIL and converting the vibration waveform into a voltage signal to be output; the data acquisition unit is connected with the vibration sensor and used for receiving the voltage signal output by the vibration sensor, amplifying and filtering the voltage signal, and converting the amplified voltage signal into a digital signal through the ADC for output; the wireless gateway is used for receiving the digital signals output by the data acquisition unit and terminating the digital signals by the terminal computer, and the terminal computer analyzes the digital signals to obtain the fault condition of the GIL. The device has strong adaptability and high sensitivity, so that the fault monitoring of the GIL is comprehensively and accurately realized, and the safety of a power grid is ensured.
Description
Technical Field
The invention relates to an electrical equipment information acquisition device, in particular to a high-adaptability GIL vibration online monitoring neural network device.
Background
The GIL is metal closed power transmission equipment insulated by adopting SF6 gas or SF6 and N2 mixed gas, has the characteristics of large power transmission capacity, flexible spatial distribution, high reliability and small environmental influence, and therefore, the GIL is very suitable for the requirements of continuous increase of power generation capacity of a modern urban power grid and construction and reconstruction of the power grid. However, the GIL has a plurality of manufacturing processes, has very fine requirements on the maintenance process, and can cause related quality problems by carelessness; and can have serious consequences in case of failure.
In the current GIL monitoring test, vibration monitoring is a very effective GIL fault monitoring means. Whether the GIL has an insulation fault or not is judged according to the detected vibration waveform signal, so that accidents of the GIL equipment can be found and solved in time, and the method has an important effect on further popularization of the GIL in a power system. However, the existing method for acquiring the vibration signals lacks high adaptability, and a neural network capable of realizing GIL vibration online monitoring is absent at present. The neural network building method is applied to many fields, but the existing method is difficult to carry along due to the particularity of the GIL device structure and the high requirement on adaptability.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide a high-adaptability GIL vibration online monitoring neural network device to effectively monitor the GIL.
In order to achieve the purpose, the technical scheme of the invention is as follows:
a high-adaptability GIL vibration online monitoring neural network device comprises a vibration sensor, a data acquisition unit, a wireless gateway and a terminal computer; wherein;
the vibration sensor is fixedly arranged on the surface of the GIL in a neural network networking mode, is in close contact with the surface of the GIL and is used for detecting the vibration waveform of the GIL and converting the vibration waveform into a voltage signal to be output;
the data acquisition unit is connected with the vibration sensor and used for receiving the voltage signal output by the vibration sensor, amplifying and filtering the voltage signal, and converting the amplified voltage signal into a digital signal through the ADC for output;
the wireless gateway is used for receiving the digital signals output by the data acquisition unit and terminating the digital signals by the terminal computer, and the terminal computer analyzes the digital signals to obtain the fault condition of the GIL.
The mode of the neural network networking is specifically as follows:
segmenting the GIL pipe bus according to a plurality of nodes, numbering each segment, sequentially numbering three-post insulators in each segment, and numbering three legs of the three-post insulators respectively;
and a vibration sensor and a data acquisition unit are arranged on each node, each node forms a neuron, and a plurality of neurons form a network shape.
The vibration sensor is connected with the data acquisition unit through a shielded cable.
The data acquisition unit comprises a program control gain amplifier, a filter, an analog-to-digital converter, a microcontroller and a wireless communication module;
the program control gain amplifier is used for amplifying the voltage signal output by the vibration sensor to a voltage signal which accords with the input range of the analog-to-digital converter;
the filter is used for extracting the voltage signal amplified by the program control gain amplifier, extracting a useful alternating current component and performing anti-aliasing low-pass filtering;
the analog-to-digital converter is used for converting the analog signal output by the filter into a digital signal and outputting the digital signal to the microcontroller;
the microcontroller packs the received digital signals and transmits the digital signals to the wireless communication module;
the wireless communication module converts the digital signals transmitted by the microcontroller into electromagnetic waves and transmits the electromagnetic waves to the air.
The data acquisition unit also comprises a lithium ion battery and solar battery power supply conversion and management module; the lithium ion battery provides working voltage for the gain amplifier, the filter, the analog-to-digital converter, the microcontroller and the wireless communication module; the solar battery power supply conversion and management module is connected with the lithium ion battery and is used for charging the lithium ion battery with the output electric energy of the solar battery.
The vibration sensor adopts a piezoelectric industrial acceleration sensor with a built-in IC.
The filter is a band-pass filter.
Compared with the prior art, the invention has the beneficial effects that:
the high-adaptability GIL vibration online monitoring neural network device adopts a neural network networking method, nodes are arranged at each position of a GIL pipe according to a certain rule, and information processing is carried out in a mode of simulating brain neural network processing and memorizing information. Each node represents a neural unit, the defects of traditional artificial intelligence based on logic symbols in the aspects of processing intuition and unstructured information are overcome, the neural network has the characteristics of self-adaption and self-organization, the neural network simulates a human neural system to collect information and sends the information to a terminal in time for analysis and processing, and the neural network has strong adaptability and high sensitivity, so that comprehensive and accurate fault monitoring is realized on GIL, and the safety of a power grid is ensured.
Drawings
Fig. 1 is a schematic structural diagram of a high-adaptability GIL vibration online monitoring neural network device according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a neural network;
fig. 3 is a signal transmission block diagram of a wireless gateway.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and detailed description.
Example (b):
referring to fig. 1-3, the high-adaptability GIL vibration online monitoring neural network device provided in this embodiment can realize long-term online acquisition of a vibration waveform of GIL and upload the vibration waveform to a data terminal, and includes a vibration sensor fixedly mounted on a surface of GIL and in close contact with the surface of GIL, and a data collector connected to the vibration sensor through a shielded cable, where the data collector is connected to a wireless gateway in a wireless manner, and is connected to a terminal computer through the wireless gateway, and the terminal computer analyzes data and obtains a fault condition of GIL.
The vibration sensor is fixedly arranged on the GIL surface in a neural network networking mode and is in close contact with the GIL surface, and the neural network networking mode specifically comprises the following steps:
the method comprises the following steps: the GIL tubular bus is segmented according to a certain node, each segment is numbered 1, 2 and 3 … …, the three-post insulators in each segment are numbered 1, 2 and 3 … … in sequence, three legs of the three-post insulators are numbered A, B, C respectively, and A1-2 is the leg A of the second three-post insulator in the first segment.
Step two: and a signal collector and a sensor are arranged on each node, each node forms a neuron, and the neurons on each node form a network shape, as shown in fig. 2. The artificial neural network system has high fault tolerance, robustness and self-organization, and can still be in an optimized working state even if the connecting line is damaged to a high degree.
Step three: the output end of the neural network outputs signals, and the signals are transmitted to the terminal through the wireless module.
In addition, in order to ensure the accuracy of the monitoring result, the vibration sensor should have the following characteristics: (1) the sensor should have high sensitivity and low noise (2) wide frequency bandwidth. The wider the frequency bandwidth, the greater the amount of information contained, the wider the type and range of faults that can be detected. (3) Has good electromagnetic shielding effect. Considering that the device under test is a strong electrical device and there may be other high voltage or high current devices in the field, these devices may interfere with the operation of the sensor through electric and magnetic fields, so the sensor should have good immunity measures. (4) The sensor output impedance is low. The low output impedance prevents the sensor output signal from being disturbed in the connection lines. (5) Small volume and light weight. The light weight of the sensor is beneficial to reducing the loading effect. (6) Wide working temperature range, dust prevention and rain prevention. According to the above specific requirements and considering the technical level of the current vibration sensor, the vibration sensor of the present embodiment adopts a piezoelectric type industrial acceleration sensor with a built-in IC, and the main technical indexes are as follows:
sensitivity: 500mV/g
3dB frequency: 20 Hz-15 kHz
Working temperature range: 40 ℃ below zero to 80 DEG C
Weight: less than 50 g
Built-in IC amplification and low input impedance; the double-layer shielding structure can effectively shield low-frequency electromagnetic field interference and high-frequency electromagnetic wave interference.
Specifically, the data collector comprises a program control gain amplifier, a filter, a high-speed ADC, an MCU (microcontroller), a wireless communication module, a lithium ion battery, and a solar battery power conversion and management module.
A Programmable Gain Amplifier (PGA), the gain of which may be selected by a Microcontroller (MCU), amplifies the output signal of the vibration sensor to an appropriate magnitude to match the input range of the ADC. The filter is a band-pass filter and has the function of extracting a useful alternating current component and performing anti-aliasing low-pass filtering. The ADC, i.e., an analog-to-digital converter, converts the analog signal output by the filter into a digital signal. And the Microcontroller (MCU) receives and packages the data of the ADC and then transmits the data to the wireless module, and the MCU is also responsible for coordinating other modules, analyzing protocols and the like. The wireless module converts the digital signals sent by the MCU into electromagnetic waves and sends the electromagnetic waves to the air. When the sunlight is suitable, the charging module charges the lithium battery by using the output electric energy of the solar battery. The DC/DC module outputs stable direct current voltage and provides working voltage for the amplifier, the filter, the ADC, the microcontroller and the wireless module. The signal transmission comprises the following specific steps:
1. vibration generated by GIL fault or vibration generated during normal work is transmitted to the surface of the GIL and detected by the vibration sensor, and the vibration sensor outputs a voltage signal;
2. a data acquisition unit connected with the vibration sensor receives the voltage signal output by the vibration sensor, amplifies and filters the voltage signal, and then converts the amplified and filtered voltage signal into a digital signal through an ADC (analog to digital converter);
3. and the internal wireless module sends the digital signal to the wireless gateway.
A programmable gain amplifier (PDA), the gain of which is selectable by a Microcontroller (MCU), amplifies the output signal of the vibration sensor to an appropriate magnitude to match the input range of the ADC.
As shown in fig. 3, the wireless module can receive or transmit a wireless signal, and when receiving the wireless signal, the wireless module converts the wireless signal into a digital signal and transmits the digital signal to the MCU (microcontroller), and the MCU transmits the digital signal to the RS232 serial port. The terminal computer can directly receive RS232 serial port signals.
The above embodiments are only for illustrating the technical concept and features of the present invention, and the purpose thereof is to enable those skilled in the art to understand the contents of the present invention and implement the present invention accordingly, and not to limit the protection scope of the present invention accordingly. All equivalent changes or modifications made in accordance with the spirit of the present disclosure are intended to be covered by the scope of the present disclosure.
Claims (6)
1. A high-adaptability GIL vibration online monitoring neural network device is characterized by comprising a vibration sensor, a data acquisition unit, a wireless gateway and a terminal computer; wherein;
the vibration sensor is fixedly arranged on the surface of the GIL in a neural network networking mode, is in close contact with the surface of the GIL and is used for detecting the vibration waveform of the GIL and converting the vibration waveform into a voltage signal to be output;
the data acquisition unit is connected with the vibration sensor and used for receiving the voltage signal output by the vibration sensor, amplifying and filtering the voltage signal, and converting the amplified voltage signal into a digital signal through the ADC for output;
the wireless gateway is used for receiving the digital signals output by the data acquisition unit and transmitting the digital signals to the terminal computer, and the terminal computer analyzes the digital signals to obtain the fault condition of the GIL;
the mode of the neural network networking is specifically as follows:
segmenting the GIL pipe bus according to a plurality of nodes, numbering each segment, sequentially numbering three-post insulators in each segment, and numbering three legs of the three-post insulators respectively;
and a vibration sensor and a data acquisition unit are arranged on each node, each node forms a neuron, and a plurality of neurons form a network shape.
2. The high-adaptability GIL vibration online monitoring neural network device as claimed in claim 1, wherein the vibration sensor is connected with the data collector through a shielded cable.
3. The high-adaptability GIL vibration online monitoring neural network device as claimed in claim 2, wherein said data collector comprises a programmable gain amplifier, a filter, an analog-to-digital converter, a microcontroller and a wireless communication module;
the program control gain amplifier is used for amplifying the voltage signal output by the vibration sensor to a voltage signal which accords with the input range of the analog-to-digital converter;
the filter is used for extracting the voltage signal amplified by the program control gain amplifier, extracting a useful alternating current component and performing anti-aliasing low-pass filtering;
the analog-to-digital converter is used for converting the analog signal output by the filter into a digital signal and outputting the digital signal to the microcontroller;
the microcontroller packs the received digital signals and transmits the digital signals to the wireless communication module;
the wireless communication module converts the digital signals transmitted by the microcontroller into electromagnetic waves and transmits the electromagnetic waves to the air.
4. The high-adaptability GIL vibration online monitoring neural network device as claimed in claim 3, wherein said data collector further comprises a lithium ion battery and solar battery power supply conversion and management module; the lithium ion battery provides working voltage for the gain amplifier, the filter, the analog-to-digital converter, the microcontroller and the wireless communication module; the solar battery power supply conversion and management module is connected with the lithium ion battery and is used for charging the lithium ion battery with the output electric energy of the solar battery.
5. The high-adaptability GIL vibration online monitoring neural network device as claimed in claim 1, wherein the vibration sensor is a piezoelectric type industrial grade acceleration sensor with a built-in IC.
6. The high-adaptability GIL vibration online monitoring neural network device, as set forth in claim 3 or claim 4, wherein said filter is a band-pass filter.
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| CN1262540A (en) * | 1999-01-28 | 2000-08-09 | 株式会社日立制作所 | Method and system for diagnosing partial discharging in gas insulator |
| CN101799367A (en) * | 2010-01-27 | 2010-08-11 | 北京信息科技大学 | Electromechanical device neural network failure trend prediction method |
| CN104698354A (en) * | 2015-03-13 | 2015-06-10 | 西安交通大学 | GIL (globalization, internationalization and localization) breakdown discharge positioning detecting system |
| CN107329933A (en) * | 2017-07-14 | 2017-11-07 | 北京知觉科技有限公司 | Fault detection method and device based on Fibre Optical Sensor vibration signal |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| JPH06274660A (en) * | 1993-03-18 | 1994-09-30 | Hitachi Ltd | Recognizing or diagnosing method |
| JPH09133732A (en) * | 1995-11-13 | 1997-05-20 | Nissin Electric Co Ltd | Abnormal vibration monitor device for electric equipment |
| CN103605074A (en) * | 2013-12-03 | 2014-02-26 | 国家电网公司 | Method and device for positioning fault of gas insulation closed switch |
| CN106353651A (en) * | 2016-09-30 | 2017-01-25 | 国家电网公司 | Fault location method of acoustic electric joint partial discharge detection based on BP (Back Propagation) network in GIS (Gas Insulated Switchgear) |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1262540A (en) * | 1999-01-28 | 2000-08-09 | 株式会社日立制作所 | Method and system for diagnosing partial discharging in gas insulator |
| CN101799367A (en) * | 2010-01-27 | 2010-08-11 | 北京信息科技大学 | Electromechanical device neural network failure trend prediction method |
| CN104698354A (en) * | 2015-03-13 | 2015-06-10 | 西安交通大学 | GIL (globalization, internationalization and localization) breakdown discharge positioning detecting system |
| CN107329933A (en) * | 2017-07-14 | 2017-11-07 | 北京知觉科技有限公司 | Fault detection method and device based on Fibre Optical Sensor vibration signal |
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