CN115753532B - Transformer oil granularity online analysis method and system based on light intensity time domain characteristics - Google Patents
Transformer oil granularity online analysis method and system based on light intensity time domain characteristics Download PDFInfo
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Abstract
The invention provides an online analysis method and system for transformer oil granularity based on light intensity time domain characteristics, wherein the method comprises the following steps: circularly sampling transformer oil on line and preprocessing; calculating the concentration value of the oil sample particles according to the light transmittance of the oil sample relative to the first laser, and judging whether a light intensity time domain autocorrelation detection mode or a light intensity time domain cross correlation detection mode is adopted according to the concentration value: the light intensity time domain autocorrelation mode carries out autocorrelation calculation according to a first scattered light signal generated by the first laser irradiation of the oil sample to obtain the average particle size of oil sample particles; the light intensity time domain cross correlation mode carries out cross correlation calculation according to a first scattered light signal generated by the first laser irradiating the oil sample and a second scattered light signal generated by the second laser irradiating the oil sample to obtain the average particle size of oil sample particles; and inputting the average particle size into a particle size distribution inversion regression network to obtain a particle size distribution curve of the oil sample particles. According to the invention, the detection mode is flexibly selected according to the concentration of the particles of the transformer oil, and the detection precision is high.
Description
Technical Field
The invention relates to the technical field of tiny particle detection, in particular to an online analysis method and an online analysis system for transformer oil granularity based on light intensity time domain characteristics.
Background
The granularity is also called particle pollution degree and refers to the number of impurities with different particle diameters in the unit volume of the insulating oil. The index reflects the particle size and the number of the impurities of the insulating oil particles, is an objective measure of the number of the particles in the insulating oil, and directly reflects the pollution degree of the insulating oil particles. The insulating oil is an important element in the transformer, plays roles of arc extinction, cooling and insulation in the operation of the transformer, and the insulating performance of the insulating oil is more and more important, and particles in the oil influence the insulating property of the transformer oil, so that the accurate measurement of the granularity of the transformer oil has important significance.
Light scattering refers to the phenomenon in which a portion of light propagates away from the original direction as it passes through an inhomogeneous medium, and light away from the original direction is called scattered light. The light intensity time domain autocorrelation method irradiates a beam of laser on a medium, and realizes measurement of particle size distribution of a sample to be measured by receiving scattered light information, and has the characteristics of realizing non-contact measurement, wide measurement range, wide measurement object, rapidness and accuracy. The laser light is scattered as it passes through the medium, and a portion of the light changes its original path to other directions where a monitor is placed to receive the scattered light signal. Molecules in the medium continuously do Brownian motion and can move towards different directions, so that scattered light of each molecule can reach a monitor to generate coherent enhancement or coherent weakening, detected light intensity can fluctuate along with time, and the fluctuation speed is related to the molecular motion speed. According to Stokes-Einstein equation, when molecules with different particle sizes do Brownian motion, the molecular motion speed with larger particle size is slower, and the molecular motion speed with smaller particle size is faster, so that the particle size can be judged by detecting the fluctuation speed. And sending the scattered light intensity signals received by the monitor into a digital correlator for carrying out autocorrelation calculation, and analyzing the calculation result by using a neural network-based transformer oil particle size distribution inversion algorithm to obtain particle swarm particle size distribution information.
At present, a plurality of methods for detecting the granularity of the transformer oil exist, and the methods suitable for online detection mainly comprise a photoresistance method and an image method. These two methods are suitable for detecting particles of several tens to several hundreds of micrometers, but it is difficult to detect particles of submicron order, so that it is difficult to improve the particle detection accuracy due to the disadvantage that the above method cannot measure particles of small particle diameter, and thus it is difficult to improve the detection and analysis accuracy of transformer performance.
Disclosure of Invention
Aiming at the technical problems in the prior art, the invention provides the online analysis method and the online analysis system for the granularity of the transformer oil based on the light intensity time domain characteristics, which can detect the insulation performance of the insulating oil in the transformer in real time, flexibly select a detection mode according to the concentration of the particles of the transformer oil, have high detection precision on the transformer oil, and can be suitable for detecting the particle size distribution of submicron particles.
According to a first aspect of the invention, there is provided an online analysis method for transformer oil granularity based on light intensity time domain characteristics, comprising:
circularly sampling transformer oil on line and preprocessing;
calculating the concentration value of the oil sample particles according to the light transmittance of the oil sample relative to the first laser, and judging to adopt a light intensity time domain autocorrelation detection mode or a light intensity time domain cross correlation detection mode according to the concentration value of the oil sample particles;
when a light intensity time domain autocorrelation mode is adopted, performing autocorrelation calculation according to a first scattered light signal generated by the irradiation of the first laser on the oil sample, so as to obtain the average particle size of oil sample particles;
when a light intensity time domain cross correlation mode is adopted, carrying out cross correlation calculation according to a first scattered light signal generated by the first laser irradiating the oil sample and a second scattered light signal generated by the second laser irradiating the oil sample, so as to obtain the average particle size of oil sample particles;
and inputting the average particle size into a trained particle size distribution inversion regression network to obtain a particle size distribution curve of the oil sample particles.
On the basis of the technical scheme, the invention can also make the following improvements.
Optionally, preprocessing the sampled transformer oil, including:
regulating the oil pressure of the oil sample to stabilize the oil pressure of the oil sample; and/or the number of the groups of groups,
detecting the temperature value of the oil sample, comparing the temperature detection value with a temperature threshold value, and adjusting the real-time temperature of the transformer oil according to the comparison result, so that the real-time temperature is kept within the temperature threshold value range.
Optionally, calculating a concentration value of the oil sample particulate matters according to the light transmittance of the oil sample relative to the first laser, and judging to adopt a light intensity time domain autocorrelation detection mode or a light intensity time domain cross correlation detection mode according to the concentration value of the oil sample particulate matters; comprising the following steps:
starting a first laser, detecting the light transmittance of the first laser after passing through the oil sample, calculating the absorption coefficient of the oil sample according to an attenuation formula of the light transmittance and the light intensity, and deducing the concentration of the sample particles according to the absorption coefficient;
comparing the sample particulate matter concentration to a concentration threshold:
when the concentration of the particles in the sample is smaller than a concentration threshold, judging that a light intensity time domain autocorrelation detection mode is adopted, and detecting a first scattered light signal generated by the first laser irradiating the oil sample;
when the concentration of the sample particles is not less than a concentration threshold, judging that a light intensity time domain cross correlation detection mode is adopted, and starting second laser, wherein the wavelength of the second laser is different from that of the first laser; and detecting a first scattered light signal generated by the first laser irradiating the oil sample and a second scattered light signal generated by the second laser irradiating the oil sample.
Optionally, performing autocorrelation calculation according to a first scattered light signal generated by irradiating the oil sample with the first laser to obtain an average particle size of the oil sample particles; the method is realized by the following calculation formula:
wherein formula (1) represents that for a polydisperse particle system, the normalized electric field autocorrelation function is that of all scattering particlesContribution of grains; the formula (2) represents the relation between the particle size and the scattering line width; τ is the delay time; e (E) s (0) E and E s (τ) the electric field intensities corresponding to the first scattered light signals at times 0 and τ, respectively;<>representing the average over a period of time, G (f) being the normalized linewidth distribution function, f being the linewidth of the scatter spectrum; q is the modulus of the first scattered light vector, K B The Boltzmann constant is adopted, T is the thermodynamic temperature of colloid, eta is the dynamic viscosity of a dispersion medium, and D is the particle size.
Optionally, performing cross-correlation calculation according to a first scattered light signal generated by the first laser irradiating the oil sample and a second scattered light signal generated by the second laser irradiating the oil sample to obtain an average particle size of the oil sample particles; the method is realized by the following calculation formula:
wherein q is a wave vector, I 1 (q) is the intensity of the first scattered light, I 2 (q) is the intensity of the second scattered light, I 1 (q, 0) first scattered light intensity at time 0, I 2 (q, τ) is
A second scattered light intensity at the instant; beta is called coherence factor, which contains a measure of both temporal coherence and spatial coherence; beta 0V Is an overlap factor that allows slightly different scattering volumes to be detected by each detector; beta MS Determined by the ratio of single scattering to multiple scattering; θ is the scattering angle, λ is the wavelength of light; equation (3) represents a cross-correlation function of normalized first scattered light intensity and second scattered light intensity; equation (4) shows the relationship between the wave vector q and the scattering angle θ and the wavelength λ of light.
According to a second aspect of the present invention, there is provided an online analysis system for transformer oil granularity based on light intensity time domain characteristics, comprising:
comprising the following steps: the device comprises a sample conveying unit, a sample pool, a particle size distribution scattering measurement unit and a sample pretreatment unit;
the sample conveying unit is used for circularly sampling transformer oil on line and conveying the transformer oil into the sample tank;
the pretreatment unit is used for pretreating the oil sample in the sample pool to enable the oil sample to be in a constant temperature and constant pressure state;
the particle size distribution scattering measurement unit is used for emitting first laser, calculating the concentration value of oil sample particles according to the light transmittance of the oil sample on the first laser, and judging whether a light intensity time domain autocorrelation detection mode or a light intensity time domain cross correlation detection mode is adopted according to the concentration value of the oil sample particles;
when a light intensity time domain autocorrelation mode is adopted, performing autocorrelation calculation according to a first scattered light signal generated by the irradiation of the first laser on the oil sample, so as to obtain the average particle size of oil sample particles;
when the light intensity time domain cross correlation mode is adopted, the device is also used for emitting second laser, and carrying out cross correlation calculation according to a first scattered light signal generated by the first laser irradiating the oil sample and a second scattered light signal generated by the second laser irradiating the oil sample to obtain the average particle size of oil sample particles;
and the average particle size is input into a trained particle size distribution inversion regression network, so that a particle size distribution curve of the oil sample particles is obtained.
Optionally, the sample conveying unit comprises a peristaltic pump, a flowmeter and a U-shaped pipe which are sequentially communicated, wherein the input end of the peristaltic pump is communicated with the transformer oil tank, and the output end of the U-shaped pipe is communicated with the input end at the bottom of the sample tank; the U-shaped pipe is arranged upside down, so that the arc-shaped end of the U-shaped pipe is flush with the output end of the top of the sample tank, and the output end of the sample tank is communicated with the transformer oil tank.
Optionally, the preprocessing unit includes voltage stabilizing module, voltage stabilizing module communicates the arc end of U-shaped pipe and the top of sample cell respectively, voltage stabilizing module applys the same micro-positive pressure for the arc end of U-shaped pipe and the top of sample cell.
Optionally, the particle size distribution scattering measurement unit comprises a first laser, a first photomultiplier, a second laser, a second photomultiplier and a digital correlator, wherein the first laser and the second laser are respectively arranged on the wall of the sample cell through optical windows, and the first photomultiplier and the second photomultiplier are respectively arranged on the inner wall of the sample cell and can adjust angles;
the first laser is matched with the first photomultiplier, the first laser is used for emitting first laser, and the first photomultiplier is used for detecting a first scattered light signal generated by the first laser irradiating the oil sample;
the second laser is matched with a second photomultiplier, the second laser is used for emitting second laser with a wavelength different from that of the first laser, and the second photomultiplier is used for detecting a second scattered light signal generated by the second laser irradiating the oil sample;
the first photomultiplier and the second photomultiplier are electrically connected with the digital correlator and are used for providing a first scattered light signal and a second scattered light signal for the digital correlator;
the digital correlator is used for carrying out autocorrelation calculation according to the first scattered light signal or carrying out cross correlation calculation according to the first scattered light signal and the second scattered light signal to obtain the average particle size of the oil sample particles; and the average particle size is also used for transmitting the average particle size to a trained particle size distribution inversion regression network so as to obtain a particle size distribution curve of the oil sample particles.
Optionally, the preprocessing unit comprises a temperature measurement module and a temperature adjustment module, wherein the temperature measurement module is arranged adjacent to the particle size distribution scattering measurement unit and is used for monitoring the real-time temperature of the oil sample; the temperature adjusting module is arranged around the outer wall of the sample tank and used for adjusting the temperature of the sample tank according to the real-time temperature of the oil sample, so that the temperature of the oil sample is kept within a preset temperature range.
According to the method and the system for online analysis of the transformer oil granularity based on the light intensity time domain characteristics, provided by the invention, the transformer oil granularity is detected online by using the light intensity time domain autocorrelation or cross correlation method according to the real-time concentration of the transformer oil, and the submicron-level particles in the transformer insulating oil are detected in real time by utilizing the characteristics of wide measurement range and small detectable particle size of the method, so that workers can monitor the working state of the transformer more accurately. According to the invention, the detection mode can be automatically switched according to the conditions of different particle concentrations, the measurement range can be effectively widened, and a more accurate particle size distribution result can be obtained; the method for measuring the light intensity time domain autocorrelation or cross correlation has the advantages of accurate and reliable measurement result, good repeatability, simple setting and high automation of measurement due to the use of a digital correlator.
Drawings
FIG. 1 is a flow chart of an online analysis method for transformer oil granularity based on light intensity time domain characteristics;
fig. 2 is a schematic structural composition diagram of an online analysis system for transformer oil granularity based on light intensity time domain characteristics.
In the drawings, the list of components represented by the various numbers is as follows:
11. a peristaltic pump; 12. a flow meter; u-shaped tube; 14. a voltage stabilizing module; 21. a sample cell; 22. a refractive index measurement module; 23. a first optical window; 24. a second optical window; 31. a first laser; 32. a first coupling optical fiber; 33. a first photomultiplier tube; 34. a second laser; 35. a second photomultiplier tube; 36. a digital correlator; 37. a second coupling optical fiber; 41. a temperature adjustment module; 42. and the temperature measuring module.
Detailed Description
The following describes in further detail the embodiments of the present invention with reference to the drawings and examples. The following examples are illustrative of the invention and are not intended to limit the scope of the invention.
Fig. 1 is a flowchart of an online analysis method for transformer oil granularity based on light intensity time domain characteristics, and fig. 2 is a schematic structural diagram of an online analysis system for transformer oil granularity based on light intensity time domain characteristics.
Referring to fig. 2, the embodiment first provides an online analysis system for transformer oil granularity based on light intensity time domain characteristics, which comprises: the sample conveying unit, the sample cell 21, the particle size distribution scattering measurement unit and the sample pretreatment unit; wherein:
the sample conveying unit is used for circularly sampling transformer oil on line and conveying the transformer oil into the sample tank 21.
More specifically, as shown in fig. 2, the sample conveying unit includes a peristaltic pump 11, a flowmeter 12 and a U-shaped tube 13 that are sequentially communicated, an input end of the peristaltic pump 11 is communicated with a transformer oil tank through a pipeline, and an output end of the U-shaped tube 13 is communicated with an input end at the bottom of the sample tank 21, so as to form a passage from the oil tank to the sample tank 21. The U-shaped pipe 13 is reversely welded on one side of the sample tank 21, so that the arc-shaped end of the U-shaped pipe 13 is flush with the output end at the top of the sample tank 21, and the pressure stabilizing control of an oil sample is facilitated; the output end of the sample tank 21 is arranged at one side away from the U-shaped pipe 13 and is communicated with a transformer oil tank through a pipeline, and the detected oil sample is conveyed back to the oil tank through a pipeline and/or another peristaltic pump, so that a loop of the transformer is formed, and the detected oil sample flows back to the transformer so as not to consume the transformer oil.
The sample cell 21 is made of stainless steel, and has a cover on the top and a movable opening.
The pretreatment unit is used for pretreating the oil sample in the sample tank 21 to enable the oil sample to be in a constant temperature and constant pressure state.
More specifically, the preprocessing unit comprises a voltage stabilizing module 14, the voltage stabilizing module 14 is respectively communicated with the arc-shaped end of the U-shaped pipe 13 and the top end of the sample tank 21, and the voltage stabilizing module 14 applies the same micro positive pressure to the arc-shaped end of the U-shaped pipe 13 and the top end of the sample tank 21, so that the liquid level height of the U-shaped pipe 13 and the top end of the sample tank 21 is equal during working, and disturbance of real-time measurement of a transformer oil sample is reduced.
It can be understood that, because the detection result of the light intensity time domain autocorrelation or cross correlation technology is interfered by dust, stray light and other problems, the system should be installed in a room with weak light and clean environment as much as possible, meanwhile, the U-shaped tube 13 and the sample cell 21 are additionally provided with the pressure stabilizing module 14, micro positive pressure slightly greater than atmospheric pressure is introduced into the pressure stabilizing module 14, and the air pressure inside the system is greater than the outside, so that dust can be reduced to enter a dynamic light scattering detection area, errors are reduced, and the experimental result is more accurate. Due to the principle of communicating vessels, the U-shaped pipe 13 and the liquid level of the sample tank 21 are at the same height, so that the pressure intensity on two sides is kept consistent as much as possible, and the disturbance to the sample in the sample tank 21 is reduced. The micro positive pressure maintained by the voltage stabilizing module 14 can also ensure that the sample cannot flow backwards, and the measurement cannot be adversely affected.
The pretreatment unit further comprises a temperature measurement module 42 and a temperature adjustment module 41. The temperature measuring module 42 is disposed adjacent to the particle size distribution scattering measurement unit, and is installed in the sample cell 21, and is used for monitoring the real-time temperature of the oil sample, and ensuring the measured temperature, i.e. the temperature during detection. It will be appreciated that the operating temperature of the transformer oil is typically high, and in order to ensure accuracy of detection, it is necessary to cool the oil sample and keep it in a relatively constant temperature range, for example, the temperature of the oil sample is rapidly reduced from several tens of hundred degrees to room temperature by the temperature adjustment module 41 after it reaches the sample cell 21, so as to ensure accuracy of measurement. As shown in fig. 2, the temperature adjusting module 41 is disposed around the outer wall of the sample cell 21, for example, around the outer circumference of the bottom of the sample cell 21, for adjusting the temperature of the sample cell 21 according to the real-time temperature of the oil sample, so that the temperature of the oil sample is maintained within a preset temperature range. The temperature adjustment may be suspended when the temperature measurement module 42 monitors a temperature within a preset temperature range. The sample cell 21 is made of stainless steel, has high heat conductivity, further improves the cooling efficiency, reduces errors and enables the measurement result to be more accurate.
As shown in fig. 2, the particle size distribution scatterometry unit is disposed in the middle of the sampling cell. Two non-interfering holes are formed on the side surface of the sampling tank for mounting a Polydimethylsiloxane (PDMS) transparent optical window, and a window is formed on the side surface of the sampling tank for the laser light path for detection to pass through. The temperature measuring module 42 is arranged at the same position as the optical window in height, so that the measured temperature, namely the temperature during detection, is ensured. The temperature adjustment module 41 covers the surface of the cuvette 21 as much as possible, but bypasses the optical window portion of the cuvette 21 to reduce the welding difficulty.
The particle size distribution scattering measurement unit comprises a first laser 31, a first photomultiplier 33, a second laser 34, a second photomultiplier 35 and a digital correlator 36, wherein the first laser 31 is mounted on the wall of the sample cell 21 through a first optical window 23, and the first laser 31 is coupled with the first optical window 23 through a first coupling optical fiber 32. The second laser 34 is mounted on the wall of the sample cell 21 through the second optical window 24, and the second laser 34 is coupled to the second optical window 24 through the second coupling optical fiber 37, so that the first laser 31 and the second laser 34 do not interfere with each other. The first photomultiplier 33 and the second photomultiplier 35 are respectively installed on the inner wall of the sample cell 21 through movable installation pieces, and the angles can be adjusted according to the test requirements, so that the angles of scattered light can be received along the outer wall of the sample cell 21 in a horizontal direction in a changing manner, and the detection angles can be set by workers.
The incident light direction of the sampling pool is perpendicular to the conveying direction of the pressure-variable oil sample, and the scattered light direction and the incident light direction are parallel to the horizontal plane and are at the same height. A refractive index measurement module 22 may be installed at a position slightly lower than the outlet of the sampling cell, for example, an online refractometer may be used to measure the refractive index of the oil sample in the sampling cell 21 in real time, and the obtained refractive index parameter may be used as a reference factor for calculating the particle size distribution, so as to reduce the error of the final calculation result.
The first laser 31 is matched with the first photomultiplier 33, the first laser 31 is used for emitting first laser, and the first photomultiplier 33 is used for detecting a first scattered light signal generated by the first laser irradiating the oil sample;
the second laser 34 is matched with the second photomultiplier 35, the second laser 34 is used for emitting second laser light with a wavelength different from that of the first laser light, and the second photomultiplier 35 is used for detecting second scattered light signals generated by the second laser light irradiating the oil sample;
the first photomultiplier tube 33 and the second photomultiplier tube 35 are electrically connected to the digital correlator 36, and are used for providing a first scattered light signal and a second scattered light signal to the digital correlator 36;
the digital correlator 36 is configured to perform an autocorrelation calculation based on the first scattered light signal, or perform a cross-correlation calculation based on the first scattered light signal and the second scattered light signal, to obtain an average particle size of the oil-like particulate matter; and the average particle size is transmitted to a trained particle size distribution inversion regression network, and parameters such as refractive index, temperature value, viscosity and refractive index of impurities of the insulating oil are combined to calculate and obtain a particle size distribution curve of the oil sample particles.
Based on the system shown in fig. 2, as shown in fig. 1, the embodiment provides an online analysis method for transformer oil granularity based on light intensity time domain characteristics, which comprises the following steps:
on-line circulating sampling transformer oil, and preprocessing an oil sample; for example, dedusting a room, opening a peristaltic pump 11, and injecting transformer oil into the system until the liquid level of the sample reaches the height of the outlet of a sample tank 21;
calculating the concentration value of the oil sample particles according to the light transmittance of the oil sample relative to the first laser, and judging to adopt a light intensity time domain autocorrelation detection mode or a light intensity time domain cross correlation detection mode according to the concentration value of the oil sample particles;
when a light intensity time domain autocorrelation mode is adopted, performing autocorrelation calculation according to a first scattered light signal generated by the irradiation of the first laser on the oil sample, so as to obtain the average particle size of oil sample particles;
when a light intensity time domain cross correlation mode is adopted, carrying out cross correlation calculation according to a first scattered light signal generated by the first laser irradiating the oil sample and a second scattered light signal generated by the second laser irradiating the oil sample, so as to obtain the average particle size of oil sample particles;
and inputting the average particle size into a trained particle size distribution inversion regression network to obtain a particle size distribution curve of the oil sample particles.
It will be appreciated that as the transformer is used and operated, the transformer oil sample often experiences a rise in concentration and an increase in particle size. Based on the defects in the background technology, the embodiment of the invention provides an online analysis method for transformer oil granularity based on light intensity time domain characteristics. The two switchable detection technical methods can be well suitable for detecting the samples, and compared with the light intensity time domain autocorrelation, the cross correlation method can better inhibit multiple scattering effect, and is more suitable for detecting large-particle high-concentration samples, so that the accuracy of detecting the granularity of the samples can be improved by selecting a more suitable granularity detection method according to the detection result of the concentration of the transformer oil, the range is enlarged, and the method is suitable for detecting oil samples containing impurity particles.
In one possible embodiment, the pre-treating of the sampled transformer oil comprises:
regulating the oil pressure of the oil sample to stabilize the oil pressure of the oil sample; for example, the pressure stabilizing module 14 is opened, and the space above the U-shaped pipe 13 and the sample cell 21 is filled with equal air pressure slightly higher than the external pressure, and the pressure is kept constant; closing the indoor light to prevent the detection result from being influenced;
and/or the number of the groups of groups,
detecting the temperature value of the oil sample, comparing the temperature detection value with a temperature threshold value, and adjusting the real-time temperature of the transformer oil according to the comparison result, so that the real-time temperature is kept within the temperature threshold value range.
In a possible embodiment, the calculating the concentration value of the oil sample particulate matter according to the light transmittance of the oil sample about the first laser, and judging to use a light intensity time domain autocorrelation detection mode or a light intensity time domain cross correlation detection mode according to the concentration value of the oil sample particulate matter; comprising the following steps:
starting a first laser, detecting the light transmittance of the first laser after passing through the oil sample, calculating the absorption coefficient of the oil sample according to an attenuation formula of the light transmittance and the light intensity, and deducing the concentration of the sample particles according to the absorption coefficient;
comparing the sample particulate matter concentration to a concentration threshold:
when the concentration of the particles in the sample is smaller than a concentration threshold, judging that a light intensity time domain autocorrelation detection mode is adopted, and detecting a first scattered light signal generated by the first laser irradiating the oil sample;
when the concentration of the sample is not less than a concentration threshold, judging that a light intensity time domain cross correlation detection mode is adopted, and starting second laser, wherein the wavelength of the second laser is different from that of the first laser; and detecting a first scattered light signal generated by the first laser irradiating the oil sample and a second scattered light signal generated by the second laser irradiating the oil sample.
It will be appreciated that the particle size distribution measurement system first uses the first laser 31 and the first photomultiplier 33 to measure the transmittance of light to calculate the concentration of the sample, and determines which particle size calculation method is more suitable. The autocorrelation method uses a first laser 31 coupled with a first coupling fiber 32 to a first optical window 23 to direct a first laser beam perpendicularly into the sample cell 21. When the laser light irradiates particles in the oil sample to be measured in the cell, the laser light is scattered, and a first photomultiplier 33 with a corresponding wavelength is arranged inside the sample cell 21 and receives the scattered light signal. Due to the brownian motion, the scattered light is coherently increased or decreased in the receiving section, and such fluctuation of the light intensity with time is detected by the first photomultiplier 33 and transmitted to the digital correlator 36 through an electric signal for calculation.
The process of autocorrelation calculation is exemplified as follows.
Defining a first scattered electric field intensity autocorrelation function as:
the corresponding first scattered light intensity autocorrelation function is:
wherein τ is the delay time; e (E) s (0) E and E s (τ) is 0 respectively
The electric field intensity corresponding to the first scattered light at the moment; i s (0) I s (τ) is 0 respectively
First scattered light intensity at a moment;<>representing an average value over a period of time;
normalization processing is carried out on the formulas (1) and (2) to respectively obtain the normalized electric field intensity and the normalized scattered light intensity, wherein the autocorrelation function is as follows:
for a polydisperse particle system, the contribution of its normalized electric field autocorrelation function to all scattering particles is shown in formula (5):
the above equation belongs to the first class of Fredholm integral equations. In formula (5), G (f) is a normalized linewidth distribution function, f is the linewidth of the scattering spectrum, and f determines the decay rate of the autocorrelation function curve: the smaller the r, the slower the curve decay, and conversely, the faster the curve decay;
according to Stokes-Einstein relation, the relation between the particle size and the scattering line width is established, as shown in formula (6):
in the formula (6), q is a modulus of the first scattered light vector, K B The Boltzmann constant is adopted, T is the thermodynamic temperature of colloid, eta is the dynamic viscosity of a dispersion medium, and D is the particle size, namely the particle size.
For the first class of Fredholm integral equations, the solution of such equations belongs to the problem of discomfort, i.e. any minor data disturbance may lead to a large deviation of the solution from the true solution, so that the computer needs a suitable inversion algorithm to obtain a solution close to the theoretical particle size distribution when processing the autocorrelation function.
The photocurrent detected by the photomultiplier carries out autocorrelation calculation to obtain a time domain signal g of scattered light intensity (2) G is calculated according to a formula (1) And calculating the electric field autocorrelation function normalized according to the formula (5) by using an accumulation method to obtain the average particle size of particles in the transformer oil, and putting the average particle size into a trained particle size distribution inversion regression network GRNN to perform inversion prediction to obtain the number relation of particles with different diameters, namely a particle size distribution curve.
Here, a training process for inverting the regression network GRNN of particle size distribution is described as including: according to the actual condition of a transformer oil sample, a simulated sample particle system is designed, an autocorrelation function of scattered light intensity is obtained according to an anomalous diffraction integral theory, an electric field autocorrelation function is obtained through calculation, noise is added to the electric field autocorrelation function, a cumulative method is used for calculating the noise-containing electric field autocorrelation function, the average particle size of the simulated particle system is obtained and is used as the input of a particle size distribution inversion regression network, and the output end of the particle size distribution inversion regression network is the particle size distribution of the simulated sample.
A cross-correlation method, a first laser 31 and a second laser 34 with different wavelengths, wherein the first laser 31 is coupled with the first optical window 23 by a first coupling optical fiber 32, the second laser 34 is coupled with the second optical window 24 by a second coupling optical fiber 37, and the two laser beams are irradiated into the sample cell 21 and focused in the same area. The scattered light receiving end uses a first photomultiplier 33 sensitive to a first scattered light wavelength and a second photomultiplier 35 sensitive to a second scattered light wavelength, and the two photomultipliers sensitive to corresponding wavelengths transmit the two scattered light signals to a digital correlator 36 for cross-correlation calculation. The method can reduce the influence of multiple scattered light, is more suitable for detecting samples with higher concentration, and the cross-correlation function of two paths of light intensity at the receiving end is as follows:
where β is called the coherence factor, which contains a measure of both temporal coherence and spatial coherence; beta 0V Is an overlap factor that allows slightly different scattering volumes to be detected by each photomultiplier tube; beta MS Determined by the ratio of single scattering to multiple scattering. The relation between the wave vector q and the scattering angle θ and the wavelength λ of light is:
since g is established 12 (2) (τ) and g (1) (τ) can be calculated from g by the digital correlator 36 12 (2) (τ) solving for the average particle size g in a computer (1) And then putting the obtained product into a trained particle size distribution inversion regression neural network to invert the particle size distribution. Thus obtaining the particle size distribution of the transformer oil sample and timely reacting the pollution degree of the insulating oil to the staff.
According to the method and the system for online analysis of the transformer oil granularity based on the light intensity time domain characteristics, provided by the embodiment of the invention, the transformer oil granularity is online detected by using the light intensity time domain autocorrelation or cross correlation method according to the real-time particle concentration of the transformer oil, and the submicron-level particles in the transformer insulating oil are detected in real time by utilizing the characteristics of wide measurement range and small detectable particle size of the method, so that workers can monitor the working state of the transformer more accurately. According to the invention, the detection mode can be automatically switched according to the conditions of different concentrations, the measurement range can be effectively widened, and a more accurate particle size distribution result can be obtained; the method of optical intensity time domain autocorrelation or cross correlation has accurate and reliable measurement results, good repeatability, simple setup, and high automation of measurement due to the use of the digital correlator 36.
In the foregoing embodiments, the descriptions of the embodiments are focused on, and for those portions of one embodiment that are not described in detail, reference may be made to the related descriptions of other embodiments.
It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. It is therefore intended that the following claims be interpreted as including the preferred embodiments and all such alterations and modifications as fall within the scope of the invention.
It will be apparent to those skilled in the art that various modifications and variations can be made to the present invention without departing from the spirit or scope of the invention. Thus, it is intended that the present invention also include such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.
Claims (8)
1. An online analysis system for transformer oil granularity based on light intensity time domain characteristics is characterized by comprising: the device comprises a sample conveying unit, a sample pool (21), a particle size distribution scattering measurement unit and a sample pretreatment unit;
the sample conveying unit is used for circularly sampling transformer oil on line and conveying the transformer oil into the sample tank (21); the sample conveying unit comprises a peristaltic pump (11), a flowmeter (12) and a U-shaped pipe (13) which are sequentially communicated, wherein the input end of the peristaltic pump (11) is communicated with a transformer oil tank, and the output end of the U-shaped pipe (13) is communicated with the input end at the bottom of the sample tank (21); the U-shaped pipe (13) is arranged in an inverted mode, so that the arc-shaped end of the U-shaped pipe (13) is flush with the output end of the top of the sample tank (21), and the output end of the sample tank (21) is communicated with the transformer oil tank;
the pretreatment unit is used for pretreating the oil sample in the sample tank (21) to enable the oil sample to be in a constant temperature and constant pressure state; the pretreatment unit comprises a pressure stabilizing module (14), wherein the pressure stabilizing module (14) is respectively communicated with the arc-shaped end of the U-shaped pipe (13) and the top end of the sample tank (21), and the pressure stabilizing module (14) applies the same micro positive pressure to the arc-shaped end of the U-shaped pipe (13) and the top end of the sample tank (21);
the particle size distribution scattering measurement unit is used for emitting first laser, calculating the concentration value of oil sample particles according to the light transmittance of the oil sample on the first laser, and judging whether a light intensity time domain autocorrelation detection mode or a light intensity time domain cross correlation detection mode is adopted according to the concentration value of the oil sample particles;
when a light intensity time domain autocorrelation mode is adopted, performing autocorrelation calculation according to a first scattered light signal generated by the irradiation of the first laser on the oil sample, so as to obtain the average particle size of oil sample particles;
when the light intensity time domain cross correlation mode is adopted, the device is also used for emitting second laser, and carrying out cross correlation calculation according to a first scattered light signal generated by the first laser irradiating the oil sample and a second scattered light signal generated by the second laser irradiating the oil sample to obtain the average particle size of oil sample particles;
and the average particle size is input into a trained particle size distribution inversion regression network, so that a particle size distribution curve of the oil sample particles is obtained.
2. The transformer oil granularity online analysis system based on the light intensity time domain characteristics according to claim 1, wherein the particle size distribution scattering measurement unit comprises a first laser (31), a first photomultiplier (33), a second laser (34), a second photomultiplier (35) and a digital correlator (36), wherein the first laser (31) and the second laser (34) are respectively installed on the wall of a sample cell (21) through optical windows, and the first photomultiplier (33) and the second photomultiplier (35) are respectively installed on the inner wall of the sample cell (21) and can adjust angles;
the first laser (31) is matched with the first photomultiplier (33), the first laser (31) is used for emitting first laser, and the first photomultiplier (33) is used for detecting a first scattered light signal generated by the irradiation of the first laser on the oil sample;
the second laser (34) is matched with the second photomultiplier (35), the second laser (34) is used for emitting second laser light with a wavelength different from that of the first laser light, and the second photomultiplier (35) is used for detecting second scattered light signals generated by the second laser light irradiating the oil sample;
the first photomultiplier (33) and the second photomultiplier (35) are electrically connected with the digital correlator (36) and are used for providing a first scattered light signal and a second scattered light signal for the digital correlator (36);
the digital correlator (36) is used for performing autocorrelation calculation according to the first scattered light signal or performing cross-correlation calculation according to the first scattered light signal and the second scattered light signal to obtain average particle size; and the average particle size is also used for transmitting the average particle size to a trained particle size distribution inversion regression network so as to obtain a particle swarm particle size distribution curve.
3. The online analysis system of transformer oil granularity based on the light intensity time domain characteristics according to claim 1, wherein the preprocessing unit comprises a temperature measurement module (42) and a temperature adjustment module (41), and the temperature measurement module (42) is arranged adjacent to the particle size distribution scattering measurement unit and is used for monitoring the real-time temperature of an oil sample; the temperature adjusting module (41) is arranged around the outer wall of the sample tank (21) and is used for adjusting the temperature of the sample tank (21) according to the real-time temperature of the oil sample, so that the temperature of the oil sample is kept within a preset temperature range.
4. An online analysis method for transformer oil granularity based on light intensity time domain characteristics based on the system of any one of claims 1-3, which is characterized by comprising the following steps:
circularly sampling transformer oil on line and preprocessing;
calculating the concentration value of oil sample particles according to the light transmittance of the oil sample relative to the first laser, and judging to adopt a light intensity time domain autocorrelation detection mode or a light intensity time domain cross correlation detection mode according to the concentration value of the oil sample particles;
when a light intensity time domain autocorrelation mode is adopted, performing autocorrelation calculation according to a first scattered light signal generated by the irradiation of the first laser on the oil sample, so as to obtain the average particle size of oil sample particles;
when a light intensity time domain cross correlation mode is adopted, carrying out cross correlation calculation according to a first scattered light signal generated by the first laser irradiating the oil sample and a second scattered light signal generated by the second laser irradiating the oil sample, so as to obtain the average particle size of oil sample particles; the first scattered light signal is acquired by a first photomultiplier, the second scattered light signal is acquired by a second photomultiplier, and the first photomultiplier and the second photomultiplier are respectively arranged in an oil sample and have adjustable angles;
and inputting the average particle size into a trained particle size distribution inversion regression network to obtain a particle size distribution curve of the oil sample particles.
5. The online analysis method of transformer oil granularity based on light intensity time domain characteristics according to claim 4, wherein the preprocessing of the sampled transformer oil comprises:
regulating the oil pressure of the oil sample to stabilize the oil pressure of the oil sample; and/or the number of the groups of groups,
detecting the temperature value of the oil sample, comparing the temperature detection value with a temperature threshold value, and adjusting the real-time temperature of the transformer oil according to the comparison result, so that the real-time temperature is kept within the temperature threshold value range.
6. The online analysis method of transformer oil granularity based on light intensity time domain characteristics according to claim 4, wherein the concentration value of oil sample particles is calculated according to the light transmittance of the oil sample with respect to the first laser, and a light intensity time domain autocorrelation detection mode or a light intensity time domain cross correlation detection mode is adopted according to the concentration value of the oil sample particles; comprising the following steps:
starting a first laser, detecting the light transmittance of the first laser after passing through the oil sample, calculating the absorption coefficient of the oil sample according to an attenuation formula of the light transmittance and the light intensity, and deducing the concentration of the sample particles according to the absorption coefficient;
comparing the sample particulate matter concentration to a concentration threshold:
when the concentration of the sample particles is smaller than a concentration threshold, judging that a light intensity time domain autocorrelation detection mode is adopted, and detecting a first scattered light signal generated by the first laser irradiating the oil sample;
when the concentration of the sample particles is not less than a concentration threshold, judging that a light intensity time domain cross correlation detection mode is adopted, and starting second laser, wherein the wavelength of the second laser is different from that of the first laser; and detecting a first scattered light signal generated by the first laser irradiating the oil sample and a second scattered light signal generated by the second laser irradiating the oil sample.
7. The online analysis method of transformer oil granularity based on light intensity time domain characteristics according to any one of claims 4-6, wherein the method is characterized in that autocorrelation calculation is performed according to a first scattered light signal generated by first laser irradiation of an oil sample, so as to obtain the average particle size of oil sample particles; the method is realized by the following calculation formula:
wherein formula (1) represents the contribution of its normalized electric field autocorrelation function to all scattering particles for a polydisperse particle system; the formula (2) represents the relation between the particle size and the scattering line width;
is a delay time; />
Is->
Respectively 0 and
the electric field intensity corresponding to the first scattered light signal at the moment;<>represents the average value over a period of time,/->
For normalized line width distribution function, +.>
Linewidths that are scatter spectra; />
Is a modulus of the first scattered light vector, +.>
Is Boltzmann constant, & gt>
Is the thermodynamic temperature of the colloid, < >>
Is the dynamic viscosity of the dispersion medium->
Is the particle size.
8. The online analysis method of transformer oil granularity based on light intensity time domain characteristics according to claim 7, wherein the cross correlation calculation is performed according to a first scattered light signal generated by the first laser irradiating the oil sample and a second scattered light signal generated by the second laser irradiating the oil sample, so as to obtain the average particle size of oil sample particles; the method is realized by the following calculation formula:
wherein ,
for the scattering vector>
For the intensity of the first scattered light, +.>
For the intensity of the second scattered light,
first scattered light intensity at time 0, +.>
Is->
A second scattered light intensity at the instant; />
Referred to as coherence factor, contains a measure of both temporal and spatial coherence; />
Is an overlap factor that allows slightly different scattering volumes to be detected by each detector; />
Determined by the ratio of single scattering to multiple scattering; />
Is a scattering angle>
Is the wavelength of light; equation (3) represents a cross-correlation function of normalized first scattered light intensity and second scattered light intensity; formula (4) represents a scattering vector->
And the scattering angle->
Light wavelength +.>
Is a relationship of (3). />
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