CN114167242A - Cable partial discharge monitoring device based on optical fiber laser sensing technology - Google Patents
Cable partial discharge monitoring device based on optical fiber laser sensing technology Download PDFInfo
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- 238000001514 detection method Methods 0.000 claims abstract description 16
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- G—PHYSICS
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- 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
- G01R31/1218—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 using optical methods; using charged particle, e.g. electron, beams or X-rays
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- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
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- 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
- G01R31/1263—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 of solid or fluid materials, e.g. insulation films, bulk material; of semiconductors or LV electronic components or parts; of cable, line or wire insulation
- G01R31/1272—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 of solid or fluid materials, e.g. insulation films, bulk material; of semiconductors or LV electronic components or parts; of cable, line or wire insulation of cable, line or wire insulation, e.g. using partial discharge measurements
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Abstract
本发明公开了一种基于光纤激光传感技术的电缆局放监测装置，包括泵浦光源、隔离器、耦合器、波分复用器、光纤激光传感器，光纤激光传感器粘接在悬臂梁上，悬臂梁粘接在电缆上，光纤激光传感器后端依次设置有非平衡干涉仪、光电转换模块，泵浦光源发出的泵浦光经过隔离器和耦合器后分为两路，经过波分复用器后进入光纤激光传感器，电缆局放时产生的振动将引起悬臂梁的振动，进而引起光纤激光传感器发出的光波长产生变化，通过非平衡干涉仪将波长的变化转换为相位的变化，最终通过光电转换模块解调出波长信息，进而得到振动信号，将振动信号通过卷积神经网络进行在线比对，反映电缆局放检测结果，判断电缆是否异常。
The invention discloses a cable partial discharge monitoring device based on fiber laser sensing technology, comprising a pump light source, an isolator, a coupler, a wavelength division multiplexer, and a fiber laser sensor. The cantilever beam is bonded to the cable, and the back end of the fiber laser sensor is provided with an unbalanced interferometer and a photoelectric conversion module in turn. After the device enters the fiber laser sensor, the vibration generated by the partial discharge of the cable will cause the vibration of the cantilever beam, which in turn causes the wavelength of the light emitted by the fiber laser sensor to change. The photoelectric conversion module demodulates the wavelength information, and then obtains the vibration signal. The vibration signal is compared online through the convolutional neural network to reflect the partial discharge detection result of the cable and determine whether the cable is abnormal.
Description
Technical Field
The invention belongs to the technical field of electric power overhaul, and particularly relates to a cable partial discharge monitoring device based on an optical fiber laser sensing technology.
Background
With the promotion of national economy and the continuous increase of power demand, the power grid scale is larger and larger, and the voltage grade is higher and higher. As an important component of an electric power system, cables play an increasingly important role in the power grid, and their state is related to the stable operation of the power grid. However, as the cable is usually buried underground, the state of the cable has strong concealment, and once a fault occurs, not only certain challenges are brought to timely finding out the fault position for rush repair, but also great economic losses are caused.
According to the statistical data of the faults of the 6 kV-500 kV cross-linked cables by the national power grid company, the reasons of the faults of the power cables are complex and various, gaps and impurities mixed in the manufacturing process of the cables, scratches and pollution caused in the laying and installation process and electric branches generated in the aging process can cause partial discharge in different degrees, and partial discharge is also an important factor for causing insulation degradation of the cables, so that the partial discharge detection covers most of the reasons of the faults of the cables, and the research on the partial discharge detection of the cables has important significance.
In the prior art, a method for detecting partial discharge of a cable mainly adopts a method of periodic test maintenance, and the offline test method makes important contribution to the power industry, but people often encounter the situation that an accident occurs soon after qualified equipment is put into operation through test maintenance. When the cable is tested, methods such as a voltage withstand test and the like are used, the cable can be damaged, the aging of the cable is accelerated, the insulation characteristic of the cable is influenced, and a cable partial discharge monitoring device is urgently needed at present, so that partial discharge detection is carried out on the cable under the condition that the cable is not damaged.
Disclosure of Invention
Aiming at the defects of the prior art, the invention aims to provide a cable partial discharge monitoring device based on an optical fiber laser sensing technology, which is used for carrying out partial discharge detection on a cable under the condition of not damaging the cable.
In order to achieve the purpose, the invention adopts the following technical scheme:
a cable partial discharge monitoring device based on an optical fiber laser sensing technology comprises a pumping light source, an isolator and a coupler which are sequentially arranged, wherein a wavelength division multiplexer is arranged at the rear end of the coupler, an optical fiber laser sensor is arranged at the rear end of the wavelength division multiplexer, the optical fiber laser sensor is adhered to a cantilever beam, the cantilever beam is adhered to a cable, an unbalanced interferometer and a photoelectric conversion module are sequentially arranged at the rear end of the optical fiber laser sensor, the pumping light source is used for emitting pumping light, the pumping light is sequentially divided into two paths after passing through the isolator and the coupler, and enters the optical fiber laser sensor after passing through the wavelength division multiplexer, vibration generated during cable partial discharge causes vibration of the cantilever beam, the cantilever beam vibration is deformed to cause the wavelength generated by the optical fiber laser sensor to change, the change of the wavelength is converted into the change of the phase through the unbalanced interferometer, and finally wavelength information is demodulated through the photoelectric conversion module, and further obtaining a vibration signal, carrying out online comparison on the vibration signal through a convolutional neural network, reflecting a cable partial discharge detection result, and judging whether the cable is abnormal.
Further, the fiber laser sensor is a DFB laser.
Further, the convolutional neural network includes convolutional layers and pooling layers.
Further, a sample library in the convolutional neural network is a vibration signal of partial discharge of the cable, and comprises a vibration signal of partial discharge caused by air gaps and impurities in main insulation of the cable, a vibration signal of partial discharge caused by tips and burrs of a conductor, and a vibration signal of partial discharge generated at a terminal of the cable.
Further, the vibration signal in the sample library is a positive sample with a correct target image, and the vibration signal in the sample library is a negative sample with an incorrect target image, and the convolutional neural network performs machine learning by using the positive sample and the negative sample.
Compared with the prior art, the cable partial discharge monitoring device based on the optical fiber laser sensing technology has the following beneficial effects:
1. the cable partial discharge monitoring device based on the optical fiber laser sensing technology provided by the invention judges whether the cable has partial discharge or not by detecting the vibration signal generated by the partial discharge, compared with the conventional periodic test maintenance method, the method can carry out partial discharge detection on the cable under the condition of not damaging the cable, protect the service life of the cable, carry out online comparison on the vibration signal through a convolutional neural network, reflect the cable partial discharge detection result, judge whether the cable is abnormal or not, and is convenient to use.
2. In terms of reliability and stability of detection results, the attenuation of the vibration signals in the cable medium is far smaller than that of the sound signals, so that the detection sensitivity and reliability are much higher than those of the ultrasonic method. Meanwhile, the vibration signal is detected, so that the anti-electromagnetic interference capability of the vibration detector is strong. The optical fiber encodes the detected information by using optical waves in detection, and the wavelength, the frequency and the like are absolute parameters, so that the optical fiber is not influenced by system loss caused by factors such as light source power fluctuation and optical fiber bending, and has very good reliability and stability.
Drawings
The present invention will be described in further detail with reference to the accompanying drawings.
Fig. 1 is a schematic structural diagram of a cable partial discharge monitoring device based on an optical fiber laser sensing technology provided by the invention.
Detailed Description
The present invention will be further described with reference to the following examples. It is to be understood that the embodiments described are only a few embodiments of the present invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1, fig. 1 is a schematic structural diagram of a cable partial discharge monitoring device based on an optical fiber laser sensing technology according to the present invention. The invention provides a cable partial discharge monitoring device based on an optical fiber laser sensing technology, which comprises a pumping light source, an isolator and a coupler which are sequentially arranged, wherein a wavelength division multiplexer is arranged at the rear end of the coupler, an optical fiber laser sensor is arranged at the rear end of the wavelength division multiplexer, the optical fiber laser sensor is adhered to a cantilever beam, the cantilever beam is adhered to a cable, an unbalanced interferometer and a photoelectric conversion module are sequentially arranged at the rear end of the optical fiber laser sensor, the pumping light source is used for emitting pumping light, the pumping light is sequentially divided into two paths after passing through the isolator and the coupler, and enters the optical fiber laser sensor after passing through the wavelength division multiplexer, the vibration generated during cable partial discharge causes the vibration of the cantilever beam, the vibration of the cantilever beam causes the change of the wavelength emitted by the optical fiber laser sensor, the change of the wavelength is converted into the change of the phase through the unbalanced interferometer, and finally wavelength information is demodulated through the photoelectric conversion module, and further obtaining a vibration signal, carrying out online comparison on the vibration signal through a convolutional neural network, reflecting a cable partial discharge detection result, and judging whether the cable is abnormal.
In some preferred embodiments, the fiber laser sensor is a DFB laser. The DFB laser has the advantages of high single-mode working stability, narrow line width, compatibility with optical fibers and the like, and can be widely applied to the fields of optical fiber communication, optical fiber sensing, spectroscopy and the like. The structure of the optical fiber optical shed determines that the period of the optical shed has high sensitivity to temperature and stress, so that the optical fiber laser can be applied to the sensing field after being packaged. If there is a change in the external environment, the structure of the light-shed changes, and the output of the laser changes. The sensor made of the DFB laser has the advantages of high precision, high sensitivity, distributed laying, no influence of electromagnetic radiation and the like.
The convolutional neural network is a feedforward neural network, the artificial neurons of which can respond to a part of surrounding units within the coverage range and has excellent performance on large-scale image processing, and comprises convolutional layers and pooling layers. And layering the convolutional neural network in another dimension, wherein the convolutional neural network comprises two layers of structures, one is a feature extraction layer, the input of each neuron is connected with a local receiving domain of the previous layer, and the local features are extracted. Once the local feature is extracted, its positional relationship with other features is also determined. The other is a feature mapping layer, each calculation layer of the network is composed of a plurality of feature mappings, each feature mapping is a plane, and the weights of all neurons on the plane are equal. The feature mapping structure adopts a sigmoid function with small influence function kernel as an activation function of the convolution network, so that the feature mapping has displacement invariance. In addition, since the neurons on one mapping surface share the weight, the number of free parameters of the network is reduced. Each convolutional layer in the convolutional neural network is followed by a computation layer for local averaging and quadratic extraction, which reduces the feature resolution.
Convolutional neural networks are used primarily to identify two-dimensional patterns of displacement, scaling and other forms of distortion invariance. Because the feature detection layer of the convolutional neural network learns through the training data, when the convolutional neural network is used, the displayed feature extraction is avoided, and the learning is implicitly carried out from the training data; moreover, because the weights of the neurons on the same feature mapping surface are the same, the network can learn in parallel, which is also a great advantage of the convolutional network relative to the network in which the neurons are connected with each other. The convolution neural network has unique superiority in the aspects of voice recognition and image processing by virtue of a special structure with shared local weight, the layout of the convolution neural network is closer to that of an actual biological neural network, the complexity of the network is reduced by virtue of weight sharing, and particularly, the complexity of data reconstruction in the processes of feature extraction and classification is avoided by virtue of the characteristic that an image of a multi-dimensional input vector can be directly input into the network.
The method comprises the steps that various types of cable partial discharge vibration signals are classified and sorted through deep learning, and a sample library in the convolutional neural network is the cable partial discharge vibration signals which comprise the vibration signals caused by air gaps in main insulation of the cable, impurities and partial discharge, the vibration signals caused by tips and burrs of a conductor and the vibration signals caused by partial discharge at a cable terminal. The vibration signals in the sample base are positive samples with correct target images and negative samples with wrong target images, and the convolutional neural network performs machine learning by using the positive samples and the negative samples.
The cable partial discharge monitoring device based on the optical fiber laser sensing technology provided by the invention judges whether the cable has partial discharge or not by detecting the vibration signal generated by the partial discharge, compared with the conventional periodic test maintenance method, the method can carry out partial discharge detection on the cable under the condition of not damaging the cable, protect the service life of the cable, carry out online comparison on the vibration signal through a convolutional neural network, reflect the cable partial discharge detection result, judge whether the cable is abnormal or not, and is convenient to use.
Thus, it should be appreciated by those skilled in the art that while a number of exemplary embodiments of the invention have been illustrated and described in detail herein, many other variations or modifications consistent with the principles of the invention may be directly determined or derived from the disclosure of the present invention without departing from the spirit and scope of the invention. Accordingly, the scope of the invention should be understood and interpreted to cover all such other variations or modifications.
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106556781A (en) * | 2016-11-10 | 2017-04-05 | 华乘电气科技（上海）股份有限公司 | Shelf depreciation defect image diagnostic method and system based on deep learning |
| CN108573225A (en) * | 2018-03-30 | 2018-09-25 | 国网天津市电力公司电力科学研究院 | A partial discharge signal pattern recognition method and system |
| CN111121945A (en) * | 2019-11-28 | 2020-05-08 | 上海电力大学 | High-sensitivity distributed transformer vibration monitoring system |
| CN112557833A (en) * | 2020-10-10 | 2021-03-26 | 国网河南省电力公司焦作供电公司 | Cable partial discharge mode identification method based on depth sample enhancement |
| KR20210046356A (en) * | 2019-10-18 | 2021-04-28 | 한전케이디엔주식회사 | Diagnosis apparatus of partial discharge |
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- 2021-12-08 CN CN202111494080.7A patent/CN114167242A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106556781A (en) * | 2016-11-10 | 2017-04-05 | 华乘电气科技（上海）股份有限公司 | Shelf depreciation defect image diagnostic method and system based on deep learning |
| CN108573225A (en) * | 2018-03-30 | 2018-09-25 | 国网天津市电力公司电力科学研究院 | A partial discharge signal pattern recognition method and system |
| KR20210046356A (en) * | 2019-10-18 | 2021-04-28 | 한전케이디엔주식회사 | Diagnosis apparatus of partial discharge |
| CN111121945A (en) * | 2019-11-28 | 2020-05-08 | 上海电力大学 | High-sensitivity distributed transformer vibration monitoring system |
| CN112557833A (en) * | 2020-10-10 | 2021-03-26 | 国网河南省电力公司焦作供电公司 | Cable partial discharge mode identification method based on depth sample enhancement |
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