CN113030066A - Method for identifying and quantitatively analyzing glucose pollution of insulator - Google Patents
Method for identifying and quantitatively analyzing glucose pollution of insulator Download PDFInfo
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Abstract
The invention discloses an insulator glucose pollution identification and quantitative analysis method, which comprises the following steps: s1, respectively irradiating the filth on the surfaces of a plurality of insulator samples by using a pulsed laser beam by using LIBS (laser induced breakdown spectroscopy), and obtaining plasma characteristic spectrum data of each insulator sample; s2, in the plasma characteristic spectrum data, the average value of the full spectrum data is used as a training sample and is input into a BP neural network for training and identification, and the trained BP neural network is obtained; s3, using LIBS to irradiate the dirt on the surface of the insulator to be tested by using the pulse laser beams with the same parameters and obtain the plasma characteristic spectrum data of the insulator to be tested; and S4, inputting the average value of the full-spectrum data into the trained BP neural network, and identifying whether the insulator to be tested contains glucose pollution or not according to the obtained output value. The method can determine the pollution type on the surface of the insulator in real time, on line and quickly, and further determine the content of glucose pollution.
Description
Technical Field
The invention relates to insulator surface pollution measurement, in particular to an insulator glucose pollution identification and quantitative analysis method.
Background
In a power transmission line, an insulator plays double roles of mechanical connection and electrical insulation between a lead and an iron tower, and mainly comprises a suspension insulator, a strain insulator, a cross arm insulator and the like. In a transformer substation or a converter station, an insulator is used for insulating or mechanically fixing a wire and a grounding body, and mainly comprises a disconnecting switch, a post insulator of a grounding switch, porcelain bushings of a voltage transformer, a current transformer and a circuit breaker, a sleeve of a transformer and the like. From the viewpoint of manufacturing materials, insulators can be classified into electric porcelain insulators, glass insulators, and composite insulators. Due to the influence of emissions of factories, traffic, agriculture, mines, life and the like, dust falling and the like on the operation of the insulator, a layer of dirty substances is gradually accumulated on the surface of the insulator. In a humid environment, the insulator may generate pollution flashover discharge, which causes pollution flashover accidents, and brings huge loss to economic development and people's life.
Among the contaminants on the surface of the insulator, there is a special contaminant, i.e., a glucose contaminant. With the aggravation of air pollution and the gradual expansion of the scale of a power grid, under the conditions of special terrain, air temperature, environment, climate and the like, special pollutants such as glucose and the like can be accumulated on the surface of the insulator, the water absorption capacity of a pollution layer is increased due to the strong moisture absorption and water retention of the glucose, so that the leakage current is increased, the initial discharge voltage of the insulator is obviously reduced, and finally, the external insulation surface discharge accident can be caused.
The existing method for measuring glucose pollution on the surface of an insulator is inductively coupled plasma spectral analysis, the method needs to manually wipe the pollution and then bring the pollution back to a laboratory to prepare a solution, a sample solution is changed into full sol through a super-atomization device and is sprayed into a plasma torch through a quartz tube at the axis, and then the type and the content of the pollution are analyzed by measuring the specific spectral line and the strength of each element and comparing the spectral line and the strength with a standard solution. The method has complex detection process and high cost, and can not directly measure the solid filth, so that the analysis efficiency is greatly reduced.
Disclosure of Invention
In order to make up for the defects, the invention provides a method for identifying and quantitatively analyzing the glucose pollution of the insulator, which can identify the pollution type on the surface of the insulator in real time, on line and quickly and obtain the glucose pollution content on the surface of the insulator.
The technical problem of the invention is solved by the following technical scheme:
an insulator glucose pollution identification method comprises the following steps:
s1, respectively irradiating the dirt on the surfaces of a plurality of insulator samples by using a pulse laser beam by using a laser-induced breakdown spectroscopy method to obtain plasma characteristic spectrum data of each insulator sample; wherein the insulator samples comprise insulator samples with common pollution but no glucose pollution and insulator samples with different glucose pollution contents;
s2, in the plasma characteristic spectrum data, the average value of full spectrum data is used as a training sample and is input into a BP neural network for training and identification, and the trained BP neural network is obtained;
s3, irradiating the dirt on the surface of the insulator to be tested by using a laser-induced breakdown spectroscopy method and using pulse laser beams with the same parameters, and obtaining plasma characteristic spectrum data of the insulator to be tested;
and S4, inputting the average value of full spectrum data in the plasma characteristic spectrum data of the insulator to be tested into the trained BP neural network, and identifying whether the insulator to be tested contains glucose pollution or not according to the obtained output value.
Further, in step S4, when the output value is 0.5 to 1.5, it is determined that the insulator to be tested contains glucose contamination, that is, the content of glucose in the contamination is greater than 0%, otherwise, it is determined that the insulator to be tested contains common contamination but no glucose contamination, that is, the content of glucose in the contamination is 0%.
Further, in the step S1, at least 5 different analysis points are respectively taken on the upper surface of each insulator sample for testing.
Further, the at least 5 different analysis points are evenly distributed over the upper surface of the insulator sample.
Further, the plasma characteristic spectrum data obtained in the steps S1 and S3 are pre-processed, including removing interference of background spectrum and normalization.
A quantitative analysis method for insulator glucose pollution comprises the following steps:
s1, respectively irradiating the dirt on the surfaces of a plurality of insulator samples by using a pulse laser beam by using a laser-induced breakdown spectroscopy method to obtain plasma characteristic spectrum data of each insulator sample; wherein the insulator samples comprise insulator samples with common pollution but no glucose pollution and insulator samples with different glucose pollution contents;
s2, in the plasma characteristic spectrum data, the average value of full spectrum data is used as a training sample and is input into a BP neural network for training and identification, and the trained BP neural network is obtained;
s3, in the plasma characteristic spectrum data of the insulator samples with different glucose contamination contents obtained in the step S1, inputting the intensity average value of the O element characteristic spectrum line and the glucose content into the trained BP neural network, and establishing a calibration relation between the intensity of the O element characteristic spectrum line and the glucose content;
s4, irradiating the dirt on the surface of the insulator to be tested, which is identified as containing glucose dirt, by using a laser-induced breakdown spectroscopy method with pulse laser beams with the same parameters, and obtaining plasma characteristic spectrum data of the insulator to be tested;
and S5, determining the glucose content of the insulator to be tested according to the characteristic spectral line intensity of the O element in the plasma characteristic spectral line data of the insulator to be tested by using the calibration relation established in the step S3.
Further, in the step S3 and the step S5, the O element characteristic line is an OI 777.539nm characteristic line.
Further, the scaling relationship in step S3 is: y-15.3792 x +2568.4, where x is the glucose content and y is the intensity of the characteristic line of OI 777.539 nm.
Further, the plasma characteristic spectrum data obtained in the steps S1 and S4 are pre-processed, including removing interference of background spectrum and normalization.
Compared with the prior art, the invention has the advantages that:
the method comprises the steps of inducing plasma on the surface of a dirt-containing insulator by generating high-energy laser pulses by utilizing a Laser Induced Breakdown Spectroscopy (LIBS) technology, collecting the plasma by utilizing an optical fiber to obtain spectral information, and classifying spectral data by training a BP neural network, so that glucose dirt and common dirt are distinguished by the LIBS technology; and establishing a calibration relation between the characteristic spectral line intensity and the glucose content according to the characteristic spectral line intensity of oxygen elements contained in the glucose contamination, and carrying out quantitative analysis on the insulator containing the glucose contamination by using the calibration relation. The method can determine the pollution type on the surface of the insulator in real time, on line and quickly, and further determine the content of glucose pollution.
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FIG. 1 is a flow chart of a method for identifying and quantitatively analyzing glucose contamination of an insulator according to an embodiment of the present invention;
FIG. 2 is a calibration curve of characteristic line intensity versus glucose content established by an embodiment of the present invention.
Detailed Description
The invention will be further described with reference to the accompanying drawings and preferred embodiments. It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict.
The invention has the conception that the Laser Induced Breakdown Spectroscopy (LIBS) is utilized to detect the dirt on the surface of the insulator, plasma is induced on the surface of the insulator containing the dirt by generating high-energy laser pulses, and the plasma emission spectrum collected by an optical fiber contains the component information of an ablated sample. When LIBS experimental parameters are unchanged, spectral signals obtained by laser ablation of glucose filth and common filth are different, and spectral data can be classified by training a BP neural network, so that the glucose filth and the common filth are distinguished by an LIBS technology; and establishing a calibration relation between the characteristic spectral line intensity and the glucose content according to the characteristic spectral line intensity of oxygen elements contained in the glucose contamination, and carrying out quantitative analysis on the insulator containing the glucose contamination by using the calibration relation.
In one embodiment, a method for identifying and quantitatively analyzing glucose contamination of an insulator comprises the following steps:
s1, respectively irradiating the dirt on the surfaces of a plurality of insulator samples by using a pulse laser beam by using a laser-induced breakdown spectroscopy method to obtain plasma characteristic spectrum data of each insulator sample; wherein the insulator samples comprise insulator samples with common pollution but no glucose pollution and insulator samples with different glucose pollution contents;
s2, in the plasma characteristic spectrum data, inputting an average value of full spectrum data (namely, taking an average value of spectral line intensities of the full spectrum data, and the same below) as a training sample into a BP neural network for training and identification to obtain the trained BP neural network;
s3, irradiating the dirt on the surface of the insulator to be tested by using a laser-induced breakdown spectroscopy method and using pulse laser beams with the same parameters, and obtaining plasma characteristic spectrum data of the insulator to be tested;
and S4, inputting the average value of full spectrum data in the plasma characteristic spectrum data of the insulator to be tested into the trained BP neural network, and identifying whether the insulator to be tested contains glucose pollution or not according to the obtained output value.
In a preferred embodiment, in step S4, when the output value is 0.5 to 1.5, it is determined that the insulator to be tested contains glucose contamination, that is, the content of glucose in the contamination is greater than 0%, otherwise, it is determined that the insulator to be tested contains common contamination but no glucose contamination, that is, the content of glucose in the contamination is 0%.
In a preferred embodiment, in step S1, at least 5 different analysis points are respectively taken on the upper surface of each insulator sample for testing.
In a preferred embodiment, the at least 5 different analysis spots are evenly distributed over the upper surface of the insulator sample.
In a preferred embodiment, the plasma characteristic spectrum data obtained in steps S1 and S3 are pre-processed, including removing interference from the background spectrum and normalization.
In a preferred embodiment, the single pulse energy of the pulsed laser beam is 60-80 mJ.
In another embodiment, a method for quantitatively analyzing glucose contamination of an insulator comprises the following steps:
s1, respectively irradiating the dirt on the surfaces of a plurality of insulator samples by using a pulse laser beam by using a laser-induced breakdown spectroscopy method to obtain plasma characteristic spectrum data of each insulator sample; wherein the insulator samples comprise insulator samples with common pollution but no glucose pollution and insulator samples with different glucose pollution contents;
s2, in the plasma characteristic spectrum data, the average value of full spectrum data is used as a training sample and is input into a BP neural network for training and identification, and the trained BP neural network is obtained;
s3, in the plasma characteristic spectrum data of the insulator samples with different glucose contamination contents obtained in the step S1, inputting the intensity average value of the O element characteristic spectrum line and the glucose content into the trained BP neural network, and establishing a calibration relation between the intensity of the O element characteristic spectrum line and the glucose content;
s4, irradiating the dirt on the surface of the insulator to be tested, which is identified as containing glucose dirt, by using a laser-induced breakdown spectroscopy method with pulse laser beams with the same parameters, and obtaining plasma characteristic spectrum data of the insulator to be tested;
and S5, determining the glucose content of the insulator to be tested according to the characteristic spectral line intensity of the O element in the plasma characteristic spectral line data of the insulator to be tested by using the calibration relation established in the step S3.
In a preferred embodiment, in step S3, the O element characteristic line is an OI 777.539nm characteristic line.
In a preferred embodiment, the scaling relationship in step S3 is: y-15.3792 x +2568.4, where x is the glucose content (%) and y is the intensity of characteristic line of OI 777.539 nm.
In a preferred embodiment, in step S1, at least 5 different analysis points are respectively taken on the upper surface of each insulator sample for testing.
In a preferred embodiment, the at least 5 different analysis spots are evenly distributed over the upper surface of the insulator sample.
In a preferred embodiment, in step S4, when the insulator to be tested is tested, at least 5 different analysis points are taken on the upper surface of the insulator to be tested for testing, and accordingly, in step S5, the average value of the characteristic spectral line intensities of the O element of all the analysis points is substituted into the calibration relationship established in step S3, and the glucose content of the insulator to be tested is calculated.
In a preferred embodiment, the plasma characteristic spectrum data obtained in steps S1 and S4 are pre-processed, including removing interference from the background spectrum and normalization.
In a preferred embodiment, the single pulse energy of the pulsed laser beam is 60-80 mJ.
The invention is further illustrated below with reference to specific examples.
The functional block diagram of the method for identifying and quantitatively analyzing the glucose contamination of the insulator in the embodiment of the invention is shown in figure 1.
(1) Remote LIBS equipment device construction
The LIBS system is mainly composed of four parts: laser instrument, light path system, controller, spectrum appearance. By generating laser pulse with extremely high power density, the laser acts on a sample through the reflection and focusing of the lens, and plasma can be generated on the surface of the insulator with dirt in an instant induction mode. By selecting proper laser energy, light receiving angle and spectrometer delay time, a spectrum signal with high signal-to-noise ratio and signal-to-back ratio can be obtained, in the example, the specifically adopted laser energy is 75mJ, the light receiving angle is 75 degrees, and the spectrometer delay time is 3 microseconds.
(2) Identification and classification of filth and establishment of calibration relation
Respectively irradiating the dirt on the surfaces of a plurality of insulator samples by using a pulse laser beam by using a laser-induced breakdown spectroscopy method to obtain plasma characteristic spectrum data of each insulator sample; wherein the insulator samples comprise insulator samples with common pollution but no glucose pollution and insulator samples with different glucose pollution contents; and respectively taking 5 different analysis points on the upper surface of the two insulator samples containing different pollution types for testing.
And preprocessing each plasma characteristic spectrum data, such as removing the interference of a background spectrum, performing normalization processing, taking the average value of the processed full spectrum data as a training sample, inputting the training sample into a BP neural network for training and identifying to obtain the trained BP neural network.
After the obtained plasma characteristic spectrum data of the insulator sample containing glucose contaminants with different contents are preprocessed (such as interference of background spectrum is removed and normalization processing is carried out), the average intensity value of the O element characteristic spectrum line and the glucose content are input into a trained BP neural network, and a calibration relation between the intensity of the O element characteristic spectrum line and the glucose content is established; specifically, the present embodiment uses the characteristic line of OI 777.539nm to establish a scaling relationship of y-15.3792 x +2568.4, where x is the glucose content (%) and y is the intensity of the characteristic line of OI 777.539nm, and preferably y is the average of the intensity of the characteristic line of OI 777.539nm at all analysis points. In the process of establishing the calibration relation, the inventor compares characteristic spectral lines of OI 777.194nm, OI 777.417nm and OI 777.539nm to respectively establish the calibration relation, and finds that the fitting effect of the calibration curve is better when characteristic spectral lines of OI 777.539nm are adopted.
(3) Actually measured insulator to be measured
Irradiating the dirt on the surface of the insulator to be tested by using a laser-induced breakdown spectroscopy method and using pulse laser beams with the same parameters, and obtaining plasma characteristic spectral data of the insulator to be tested;
and inputting the average value of full spectrum data in the plasma characteristic spectrum data of the insulator to be tested into the trained BP neural network, and identifying whether the insulator to be tested contains glucose pollution or not according to the obtained output value. Specifically, when the output value is 0.5-1.5, the insulator to be tested is judged to contain glucose pollution, namely the glucose content in the pollution is more than 0%, otherwise, the insulator to be tested is judged to contain common pollution but no glucose pollution, namely the glucose content in the pollution is 0%. The embodiment of the application tests 15 insulators, and the output values are shown in the following table 1:
TABLE 1
| Insulator serial number | 1 | 2 | 3 | 4 | 5 |
| Output value | 0.9515 | 1.0050 | 0.9680 | 1.0337 | 1.1490 |
| Insulator serial number | 6 | 7 | 8 | 9 | 10 |
| Output value | 2.2342 | 2.2801 | 1.4595 | 1.6077 | 1.5930 |
| Insulator serial number | 11 | 12 | 13 | 14 | 15 |
| Output value | 2.7652 | 2.8151 | 2.5910 | 2.7350 | 2.6299 |
As can be seen from the above table, the insulators with numbers 1-5 and 8 contain glucose contamination, while the insulators with numbers 6-7 and 9-15 contain ordinary contamination (the insulator without glucose contamination is defined as an insulator with ordinary contamination in the present application). The pollution types of the 15 insulators are identified by the conventional method, and the obtained conclusion is consistent with that obtained by the method.
Irradiating the dirt on the upper surface of the insulator to be tested which is identified as containing the glucose dirt by using a laser-induced breakdown spectroscopy method (5 uniformly distributed analysis points are selected on the upper surface for testing) with pulse laser beams with the same parameters, and obtaining plasma characteristic spectrum data of the insulator to be tested (or directly selecting the obtained plasma characteristic spectrum data of the insulator to be tested which is identified as containing the glucose dirt); and preprocessing the plasma characteristic spectrum data, such as removing interference of a background spectrum, and performing normalization processing. And substituting the average intensity values of OI 777.539nm characteristic spectral lines of all the analysis points into the calibration relation, and calculating the glucose pollution content on the surface of the insulator to be measured.
Randomly selecting one of insulators with the serial numbers of 1-5 and 8 and containing glucose pollution to test, wherein the average intensity value of characteristic spectral lines of OI 777.539nm of the insulator to be tested is 1900.932, the glucose content is 43.4% according to a calibration relation, the actual glucose content on the surface of the insulator is 40% by using the conventional method, and the measurement error is 8.5%.
The upper surface of the insulator described herein generally refers to the surface of the insulator which faces upward in use (surface with more accumulated dirt), and the dirt on the upper surface of the insulator can be generally regarded as dirt on the entire insulator.
The foregoing is a more detailed description of the invention in connection with specific preferred embodiments and it is not intended that the invention be limited to these specific details. For those skilled in the art to which the invention pertains, several equivalent substitutions or obvious modifications can be made without departing from the spirit of the invention, and all the properties or uses are considered to be within the scope of the invention.
Claims (9)
1. The method for identifying the glucose pollution of the insulator is characterized by comprising the following steps of:
s1, respectively irradiating the dirt on the surfaces of a plurality of insulator samples by using a pulse laser beam by using a laser-induced breakdown spectroscopy method to obtain plasma characteristic spectrum data of each insulator sample; wherein the insulator samples comprise insulator samples with common pollution but no glucose pollution and insulator samples with different glucose pollution contents;
s2, in the plasma characteristic spectrum data, the average value of full spectrum data is used as a training sample and is input into a BP neural network for training and identification, and the trained BP neural network is obtained;
s3, irradiating the dirt on the surface of the insulator to be tested by using a laser-induced breakdown spectroscopy method and using pulse laser beams with the same parameters, and obtaining plasma characteristic spectrum data of the insulator to be tested;
and S4, inputting the average value of full spectrum data in the plasma characteristic spectrum data of the insulator to be tested into the trained BP neural network, and identifying whether the insulator to be tested contains glucose pollution or not according to the obtained output value.
2. The method for identifying the glucose contamination of the insulator according to claim 1, wherein in step S4, when the output value is 0.5 to 1.5, it is determined that the insulator to be tested contains glucose contamination, i.e., the glucose content in the contamination is greater than 0%, otherwise, it is determined that the insulator to be tested contains common contamination but no glucose contamination, i.e., the glucose content in the contamination is 0%.
3. The method for identifying insulator glucose contamination according to claim 1 or 2, wherein in step S1, at least 5 different analysis points are respectively taken on the upper surface of each insulator sample for testing.
4. The method of identifying insulator glucose contamination of claim 3, wherein the at least 5 different analysis points are evenly distributed on the top surface of the insulator sample.
5. The method for identifying glucose contamination of an insulator according to claim 1 or 2, wherein the plasma characteristic spectrum data obtained in steps S1 and S3 are pre-processed, including removing interference of background spectrum and normalization.
6. A quantitative analysis method for insulator glucose pollution is characterized by comprising the following steps:
s1, respectively irradiating the dirt on the surfaces of a plurality of insulator samples by using a pulse laser beam by using a laser-induced breakdown spectroscopy method to obtain plasma characteristic spectrum data of each insulator sample; wherein the insulator samples comprise insulator samples with common pollution but no glucose pollution and insulator samples with different glucose pollution contents;
s2, in the plasma characteristic spectrum data, the average value of full spectrum data is used as a training sample and is input into a BP neural network for training and identification, and the trained BP neural network is obtained;
s3, in the plasma characteristic spectrum data of the insulator samples with different glucose contamination contents obtained in the step S1, inputting the intensity average value of the O element characteristic spectrum line and the glucose content into the trained BP neural network, and establishing a calibration relation between the intensity of the O element characteristic spectrum line and the glucose content;
s4, irradiating the dirt on the surface of the insulator to be tested, which is identified as containing glucose dirt, by using a laser-induced breakdown spectroscopy method with pulse laser beams with the same parameters, and obtaining plasma characteristic spectrum data of the insulator to be tested;
and S5, determining the glucose content of the insulator to be tested according to the characteristic spectral line intensity of the O element in the plasma characteristic spectral line data of the insulator to be tested by using the calibration relation established in the step S3.
7. The method for quantitatively analyzing glucose contamination in an insulator according to claim 6, wherein in the steps S3 and S5, the O element characteristic line is an OI 777.539nm characteristic line.
8. The method for quantitatively analyzing glucose pollution in an insulator according to claim 7, wherein the calibration relation in the step S3 is: y-15.3792 x +2568.4, where x is the glucose content and y is the intensity of the characteristic line of OI 777.539 nm.
9. The method for quantitatively analyzing the glucose pollution in the insulator according to claim 6, wherein the pre-processing of the plasma characteristic spectrum data obtained in the steps S1 and S4 comprises removing the interference of the background spectrum and normalizing.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN113624712A (en) * | 2021-08-16 | 2021-11-09 | 云南电网有限责任公司电力科学研究院 | Insulator contamination degree detection method and device based on terahertz time-domain spectroscopy |
| CN113624713A (en) * | 2021-08-16 | 2021-11-09 | 云南电网有限责任公司电力科学研究院 | Method and device for detecting filthy components on surface of post insulator |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN113624712A (en) * | 2021-08-16 | 2021-11-09 | 云南电网有限责任公司电力科学研究院 | Insulator contamination degree detection method and device based on terahertz time-domain spectroscopy |
| CN113624713A (en) * | 2021-08-16 | 2021-11-09 | 云南电网有限责任公司电力科学研究院 | Method and device for detecting filthy components on surface of post insulator |
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