CN117890773B - Electrode fluorination evaluation method, medium and system based on metal particle lifting - Google Patents
Electrode fluorination evaluation method, medium and system based on metal particle lifting Download PDFInfo
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- G06T7/20—Analysis of motion
- G06T7/246—Analysis of motion using feature-based methods, e.g. the tracking of corners or segments
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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
- G01R31/327—Testing of circuit interrupters, switches or circuit-breakers
- G01R31/3271—Testing of circuit interrupters, switches or circuit-breakers of high voltage or medium voltage devices
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Abstract
本发明提供了一种基于金属颗粒启举的电极氟化评估方法、介质及系统，属于电极氟化评估技术领域，包括：获取金属颗粒在待测GIS中的运动图像并获取金属颗粒的运动轨迹；提取反弹轨迹段；对于反弹轨迹段，计算金属颗粒的带电量；根据金属颗粒的带电量以及金属颗粒与GIS中电极接触时间，计算金属颗粒的充电速度；将多个反弹轨迹段对应的金属颗粒充电速度在数据库中进行匹配，得到多个电极氟化指数，包括氟化温度和氟化时长；对得到的多个电极氟化指数进行聚类，以聚类中心作为待测GIS电极的氟化指数。解决了现有技术中判断GIS电极氟化效果的方法是通过实验来验证，不能实现快捷的电极氟化的快速判断，影响维护效率的技术问题。
The present invention provides an electrode fluorination evaluation method, medium and system based on metal particle lifting, which belongs to the field of electrode fluorination evaluation technology, including: obtaining a motion image of metal particles in a GIS to be tested and obtaining the motion trajectory of the metal particles; extracting a rebound trajectory segment; for the rebound trajectory segment, calculating the charge of the metal particles; calculating the charging speed of the metal particles according to the charge of the metal particles and the contact time between the metal particles and the electrodes in the GIS; matching the charging speeds of the metal particles corresponding to multiple rebound trajectory segments in a database to obtain multiple electrode fluorination indexes, including fluorination temperature and fluorination time; clustering the obtained multiple electrode fluorination indexes, and taking the cluster center as the fluorination index of the GIS electrode to be tested. The method solves the technical problem that the method for judging the fluorination effect of GIS electrodes in the prior art is to verify by experiment, which cannot realize the rapid judgment of electrode fluorination and affects the maintenance efficiency.
Description
Technical Field
The invention belongs to the technical field of electrode fluorination evaluation, and particularly relates to an electrode fluorination evaluation method, medium and system based on metal particle lifting.
Background
The problem of high voltage dc power transmission and transformation is always more and more complex than that of high voltage ac power transmission and transformation due to the unchanged electric field direction of the dc field and the polarity of the emitted (or injected) charges, etc. Therefore, how to inhibit the accumulation of charges on the surface of the insulator or inside the insulator of the power transmission and transformation equipment, how to reduce the influence of metal particle pollution in the gas insulation equipment, how to improve the external insulation performance of the power transmission and transformation equipment, and the like are problems which must be faced by ultra-high voltage direct current transmission engineering and are the most concerned problems of researchers and engineering technicians at home and abroad. In the GIS field, the problem of insulator surface charge accumulation is a key insulation problem affecting the safe and reliable operation of SF6 gas insulation high-voltage equipment, and the problem is generally reduced by adopting a mode of fluorinating the electrode surface, so that the influence of the problem is reduced.
Because the fluorinated layer of the electrode can be lost along with time accumulation in the use process, the electrode fluorination effect is reduced, and the method for judging the GIS electrode fluorination effect in the prior art is verified through experiments, so that the rapid electrode fluorination judgment cannot be realized, and the maintenance efficiency is affected.
Disclosure of Invention
In view of the above, the invention provides an electrode fluorination evaluation method, medium and system based on metal particle initiation, which can solve the technical problems that the method for judging the electrode fluorination effect of a GIS in the prior art is verified through experiments, can not realize rapid electrode fluorination judgment and affects maintenance efficiency.
The invention is realized in the following way:
The first aspect of the invention provides an electrode fluorination evaluation method based on metal particle initiation, which comprises the following steps:
S10, acquiring a moving image of metal particles in a GIS to be detected;
s20, acquiring a motion trail of the metal particles according to the motion image;
s30, acquiring a plurality of track sections in which metal particles exist in the motion track of the metal particles, are contacted with electrodes in the GIS and are charged to rebound to the highest point, and recording the track sections as rebound track sections;
S40, for the rebound track section, calculating the charge quantity of the metal particles by combining the gravity of the metal particles and the intensity of an electric field in the GIS;
S50, calculating the charging speed of the metal particles according to the charge quantity of the metal particles and the contact time of the metal particles with the electrodes in the GIS;
S60, matching the charging speeds of the metal particles corresponding to the rebound track sections in a preset charging speed-electrode fluorination database to obtain a plurality of electrode fluorination indexes, wherein the electrode fluorination indexes comprise fluorination temperature and fluorination duration;
And S70, clustering the obtained electrode fluorination indexes, and taking a clustering center as the fluorination index of the GIS electrode to be detected.
On the basis of the technical scheme, the electrode fluorination evaluation method based on metal particle initiation can be further improved as follows:
the step of obtaining the moving image of the metal particles in the GIS to be detected specifically comprises the following steps: the high-speed camera equipment is used for acquiring metal particle moving images and preprocessing, including distortion correction, filtering and enhancement.
Further, the step of obtaining the motion trail of the metal particles specifically includes: identifying a localized metallic particle region in the moving image; and recording a coordinate point sequence and performing track curve fitting to obtain a motion track.
Further, the step of obtaining a plurality of track segments where metal particles contact with electrodes in the GIS and are charged and rebound to the highest point in the motion track of the metal particles specifically includes: analyzing the characteristic points of the motion track curve, extracting the descending and ascending critical points, judging whether the critical points correspond to collision rebound, and extracting the rebound corresponding curve section as a rebound track.
Further, the step of calculating the charge amount of the metal particles specifically includes: simulating and calculating the distribution of the electric field in the GIS; and acquiring metal particle attribute parameters and determining the motion state and the charge quantity of the metal particles at each moment.
Further, the step of calculating the charging speed of the metal particles specifically includes: according to the motion state and the charge quantity of the metal particles at each moment, obtaining characteristic parameters of the rebound section; the charge amount is calculated by applying the law of conservation of charge, the charge rate is obtained by dividing the contact electrode duration.
Further, the step of obtaining a plurality of electrode fluorination indexes specifically comprises: searching the closest matching item in a pre-built charging speed-fluorination index database according to the calculated charging speed result, and extracting the corresponding fluorination temperature and duration index.
Further, the method for obtaining the closest matching item is cosine similarity.
A second aspect of the present invention provides a computer readable storage medium, wherein the computer readable storage medium has stored therein program instructions, which when executed, are configured to perform an electrode fluorination evaluation method based on metal particle lift as described above.
A third aspect of the present invention provides an electrode fluorination assessment system based on metal particle priming, comprising the computer readable storage medium described above.
Compared with the prior art, the electrode fluorination evaluation method, medium and system based on metal particle lifting provided by the invention have the beneficial effects that: according to the invention, the motion rule of the metal particles is obtained based on an image analysis method, the charging loss condition between the metal particles and the electrodes is directly calculated by combining a charge transfer model, and a quantitative GIS local electrode fluorination index is generated in a data matching mode.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the description of the embodiments of the present invention will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of an electrode fluorination evaluation method based on metal particle priming provided by the invention;
FIG. 2 is a schematic illustration of an experimental environment;
FIG. 3 is a schematic illustration of metal particle pellet size;
FIG. 4 is a graph of a similar stepped up voltage for an epoxy insulation flashover test;
fig. 5 is a schematic view of the movement trace of the metal particle pellets.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.
As shown in fig. 1, the first aspect of the present invention provides a method for evaluating electrode fluorination based on metal particle activation, which comprises the following steps:
S10, acquiring a moving image of metal particles in a GIS to be detected;
s20, acquiring a motion trail of the metal particles according to the motion image;
s30, acquiring a plurality of track sections in which metal particles exist in the motion track of the metal particles, are contacted with electrodes in the GIS and are charged to rebound to the highest point, and recording the track sections as rebound track sections;
S40, for the rebound track section, calculating the charge quantity of the metal particles by combining the gravity of the metal particles and the intensity of an electric field in the GIS;
S50, calculating the charging speed of the metal particles according to the charge quantity of the metal particles and the contact time of the metal particles with the electrodes in the GIS;
S60, matching the charging speeds of the metal particles corresponding to the rebound track sections in a preset charging speed-electrode fluorination database to obtain a plurality of electrode fluorination indexes, wherein the electrode fluorination indexes comprise fluorination temperature and fluorination duration;
And S70, clustering the obtained electrode fluorination indexes, and taking a clustering center as the fluorination index of the GIS electrode to be detected.
The following describes in detail the specific embodiments of the above steps:
s10, obtaining a moving image of metal particles in a GIS to be detected:
1) And a high-speed camera device is arranged in the closed cabin with the GIS test device so as to ensure that a camera view can completely cover the movable range of metal particles in the GIS and provide necessary illumination facilities. High-speed imaging apparatuses generally employ industrial cameras capable of providing an imaging frequency of at least 240 frames/sec or more. An alternative model such as a Baumer LXG-80 high speed camera provides a maximum frame rate of 420 frames/sec.
2) After metal particles with different sizes, materials and numbers are arranged in the GIS movable cabin, the required imaging resolution and the required field of view range of the camera are calculated. In general, it is preferable to ensure that the individual pixel size is not more than 1/3 of the metal particle diameter to obtain a sufficiently clear image of the metal particles. And meanwhile, considering the movable range of the metal particles in the cabin, determining a proper lens, and obtaining a view field which is enough to cover the whole GIS movable range.
3) And the relative position and posture relation between the camera equipment and the GIS equipment are accurately measured and adjusted, so that the camera equipment can be independently and stably aligned to the internal area of the GIS equipment. And meanwhile, the camera equipment is connected with the rear-end image acquisition and processing equipment, so that hardware connection is completed and software calibration is performed.
4) A moving image sequence of the metal particles is acquired and stored using imaging software. And simultaneously recording important reference data such as image serial numbers, time stamps (time stamp), illumination parameters, camera setting parameters and the like of each key signal parameter information. Before each image sequence, a calibration plate is used for shooting a reference image, and the reference image is used as the basis for image distortion correction and pixel calibration.
5) The appropriate image sequence acquisition duration is set to ensure that a sufficient number of motion sequences are contained in the database and that each sequence covers the complete motion phases of flying, falling, colliding electrodes, bouncing, etc. from the particles. It is generally recommended to acquire at least 10-20 images of the sequence of valid crash motions.
S20, according to the moving image, acquiring a moving track of the metal particles:
1) The metal particle moving image sequence stored in the step S10 is read, and sequence segmentation is carried out, wherein each segment is a complete image sequence from entering the field of view to leaving the field of view by using single gold particles.
2) Each image sequence is preprocessed, and the preprocessing mainly comprises distortion correction, filtering denoising, image enhancement and the like. And (3) calibrating internal and external parameters of the camera by adopting the reference image stored in the S10, and eliminating the influence of lens distortion. Image noise is then suppressed using algorithms such as median filtering, gaussian filtering, etc. And finally, performing contrast and definition optimization such as histogram equalization.
3) Parameters of proper sensitivity are set, and an image segment classification algorithm based on color and shape characteristics is used for identifying and positioning metal particle targets in the preprocessed image. The metal particle region in the image is determined mainly by utilizing the characteristic that the color of the metal particles is obviously distinguished from the background. Common algorithms include region growing methods based on HSV color space models.
4) And traversing each image sequence, and recording the coordinates of the successfully detected characteristic points of the metal particles in each frame. And reorganizing the coordinate point sequence of the whole frame, and filling a possible missing detection frame to obtain a discrete point set representation of the track of the metal particles in the current image sequence.
5) And filtering and smoothing the detected discrete track points by adopting an Hanning window function, a Gaussian function and other algorithms according to the distance relation of the feature points detected by the metal particles between adjacent sequence frames, calculating a light curve equation model by using the original discrete points, and carrying out fitting expression on the motion track of the metal particles.
S30, a plurality of track sections in which metal particles are in contact with electrodes in the GIS and are charged and rebound to the highest point in the motion track of the metal particles are obtained and are recorded as rebound track sections:
1) And (3) analyzing key characteristic points such as inflection points, extremum and the like of the curve based on the metal particle motion track curve represented by the mathematical expression obtained in the step (S20). And extracting inflection points appearing in the free falling process of the metal particles and vertexes appearing in the rebound rising process, and distinguishing the inflection points and the vertexes into two categories of a falling critical point and a rising critical point.
2) And judging whether the critical points correspond to collision rebound behaviors caused by contact of the metal particles with the electrode below according to the coordinate values, the occurrence time and other information of the two types of critical points and by combining the shape characteristics of the curve. The main method is to analyze whether the curve trend is in the corresponding ascending process after the critical point is reduced.
3) And for the specific point pair which is judged to be the collision rebound critical point, extracting a curve segment between the specific point pair as a track section of the rebound process. And (3) cutting out the rebound corresponding part in the original track by adopting curve cutting and other operations to obtain the mathematical representation of the rebound curve segment.
4) And repeating the judging and extracting processes, identifying all rebound curve segments in the whole motion track, wherein each segment is represented by a descending critical point and an ascending critical point and corresponds to a complete collision rebound behavior. And (5) intensively and uniformly storing all the obtained rebound curve segments to finish the extraction and acquisition of the rebound track segments.
S40, for the rebound track section, the charged quantity of the metal particles is calculated by combining the gravity of the metal particles and the intensity of the electric field inside the GIS:
1) Reconstructing a three-dimensional geometric model of the interior of the GIS to be detected in finite element analysis software, loading actual electric field control parameters, and performing simulation calculation to obtain the space distribution rule of the electrostatic field of the interior region of the GIS. And establishing a lookup table database of electric field distribution results.
2) And acquiring physical attribute parameters of the metal particles, including data such as shape size, material density, volume quality and the like. These parameters may be obtained from a materials data table or by actual measurements.
3) And extracting the data of each rebound track segment stored in the step S30, wherein the data comprise parameters such as rebound motion duration, motion start point coordinates, motion end point coordinates and the like.
4) And setting a kinematic analysis model and a collision dynamics model, and determining the motion speed, the stress, the acceleration and the charge quantity of the metal particles at each moment of rebound motion by adopting a numerical calculation method according to the rebound parameters, the electric field distribution lookup table, the metal particle attribute parameters, the Newton's law and other principles.
5) And storing and outputting a data curve or characteristic value of the charge quantity of the metal particles in the whole rebound movement process. The maximum charge value of the curve is extracted as a final metal particle charge amount calculation result.
S50, calculating the charging speed of the metal particles according to the charge quantity of the metal particles and the contact time of the metal particles with the electrodes in the GIS:
1) Extracting the charging quantity data of the metal particles in the motion of each rebound segment obtained in the step S40;
2) Extracting key characteristic parameters of each rebound section, including rebound duration, contact duration of metal particles and electrodes and other data;
3) Establishing a charge quantity calculation model based on a charge quantity curve; determining the electric charge quantity obtained by the metal particles from the electrode by combining with the duration parameter of the contact electrode and applying principles of conservation of electric charge and the like;
4) Dividing the obtained charge quantity by the contact electrode duration, and calculating the average charge transfer rate, namely the charging speed of the metal particles;
5) And storing charging speed results corresponding to the rebound segments, visually displaying, drawing a charging speed curve or a statistical histogram and the like.
S60, matching the charging speeds of the metal particles corresponding to the rebound track segments in a preset charging speed-electrode fluorination database to obtain a plurality of electrode fluorination indexes, wherein the electrode fluorination indexes comprise fluorination temperature and fluorination duration:
1) According to the motion test of the metal particles in the GIS of electrode fluorination, a relational database of charging speed-fluorination index is constructed. The database reflects the mapping of charge rate to fluorination index.
2) Obtaining the charging speed results of the metal particles of each rebound section output in the step S50;
3) Searching the matching item closest to the charging speed values in a pre-constructed database, and extracting the corresponding fluorination index which comprises the indexes such as electrode loss temperature, loss duration and the like which are measured through experiments.
And S70, clustering the obtained electrode fluorination indexes, and taking a clustering center as the fluorination index of the GIS electrode to be detected.
1) And (3) extracting key characteristic parameters from the plurality of matching electrode fluorination index data sets obtained in the step (S60), and constructing a characteristic vector by using methods such as principal component analysis.
2) And clustering the data set based on the feature vector of the fluorination index by using a clustering algorithm such as K-means, fuzzy C-means and the like. And extracting category center points of the clusters.
3) Comparing electrode fluorination temperatures and time indexes of different clustering center points, and selecting one center point with the largest index value.
4) And taking the fluorination temperature and the fluorination duration of the maximum index center as the lumped results of all rebound sections, and finally outputting the result as the estimated fluorination index of the electrode area of the GIS equipment to be detected.
The following is a specific example of the above steps S20-S70:
S20, acquiring the motion trail of the metal particles according to the motion image
Image preprocessing:
the lens distortion of I n is eliminated using the internal reference matrix M in of I ref:
I′n＝f(In,Min)
Where f () represents a distortion correction mapping function.
Gaussian filtering and noise reduction are carried out:
I″n＝g(I′n，σ)
Where g () represents a gaussian filter function and σ is a filter kernel parameter.
Target detection and tracking:
On the preprocessed image I "n, the metal particle target region B k is identified by color and shape feature judgment:
Bk＝h(I″n(k),Tc,Ts)
where h () represents the detection decision function and T c,Ts is the color and shape similarity threshold.
Combining all sequence expressionsTracking to obtain a particle coordinate sequence:
Ptrack＝track(B)
fitting a motion trail:
And (3) applying a curve fitting method to the P track to obtain a motion trail curve equation of the particles:
Ctrack＝fit(Ptrack)
Where fit () represents the track fit function.
S30, acquiring a plurality of track sections in which metal particles exist in the motion track of the metal particles, contact with electrodes in the GIS and are charged and rebound to the highest point, and recording the track sections as rebound track sections
Finding an inflection point on a motion trail curve:
Pturning＝findTurningPoints(Ctrack)
Wherein findTurningPoints () returns all the inflection points of the trajectory curve.
Identifying the inflection point belonging to collision rebound according to the motion direction attribute of the inflection point:
I.e., a set of inflection points at which rising acceleration occurs after falling acceleration.
Extracting a curve part corresponding to the P collision section as a rebound section:
For each set of consecutive collision inflection points (p i,pj), a curve therebetween C rebound＝split(Ctrack,pi,pj is extracted.
All rebound curve sets C reb＝{Crebound1,Crebound2 were finally obtained.
S40, for the rebound track section, calculating the charge quantity of the metal particles by combining the gravity of the metal particles and the intensity of the internal electric field of the GIS
Establishing a GIS internal electric field calculation model:
E＝fEM(G,Ve)
Wherein G represents geometric information of the GIS internal structure, V e is an applied voltage value, E is a solved electric field distribution, and f EM is a solution algorithm.
Determining the physical properties of individual metal particles:
Mass m, shape factor K.
For each segment of the rebound process C reboundi, its time sequence T i and coordinate sequence P i are extracted.
Constructing particle kinematics and a kinetic equation:
Wherein, Respectively represent the gravity, the electrostatic force and the collision force,Corresponding to acceleration speed and displacement.
Charge amount calculation:
Finally, a charging amount time sequence Q i (t) of each rebound segment is obtained, wherein, Q: representing the charge of the metal particles in coulombs (C); k: the shape coefficient of the metal particles is represented as a dimensionless constant, and reflects the influence of the geometric shape of the particles on the charge quantity of the particles; An electrostatic field distribution vector representing the internal region of the GIS, the magnitude of which reflects the electric field intensity distribution in volts per meter (V/m); representing the tiny displacement vector of the metal particles under the action of an electric field in the movement process, wherein the unit is meter (m); symbol ≡: representing integration of the whole motion trail process of the metal particles; symbol @: representing the vector inner product.
S50, calculating the charging speed of the metal particles according to the charge quantity of the metal particles and the contact time of the metal particles with the electrodes in the GIS
Extraction contact electrode time t c:
For the charge sequence Q i (t), the time interval when Q increases from zero is t c.
The average charge rate is:
repeating the above process to obtain the charge rate set of all rebound sections
S60, matching the charging speeds of the metal particles corresponding to the rebound track sections in a preset charging speed-electrode fluorination database to obtain a plurality of electrode fluorination indexes including a fluorination temperature and a fluorination duration
The matching database contains charge ratesCorrespondence with the set of fluorination indices I f＝(Tf,tf).
For each resulting charge rateSearching a database for the nearest known rate value
Extracting the known rateThe corresponding fluorination index I fj is used as the matching result of the current bounce segment.
The process is repeated to obtain a series of matching fluorination indexes corresponding to all the rebound segments.
S70, clustering the obtained electrode fluorination indexes, wherein the clustering center is used as the fluorination index of the GIS electrode to be tested
The foregoing process yields a plurality of sets of fluorination indices I f＝{If1,If2.
Clustering using a K-means algorithm or the like:
C＝kmeans(If)
Where C represents the clustering result and kmeans represents the clustering function.
And selecting the class c m with the largest class number as a final result, and taking the clustering center of c m, namely the characteristic mean value, as a GIS fluorination index.
The following is a related description of the construction of an electrical velocity-fluorination index database for motion tests of metal particles in GIS of electrode fluorination:
Principle of experiment: whether in a direct current electric field or an alternating current electric field, the forces to which the particles are subjected are mainly electric field forces, gravity forces, electrode support or recoil forces (when in contact with the electrodes) and electric field gradient forces (when the electric field is non-uniform). Although the drag and buoyancy of the ambient atmosphere exists when the particles move between the electrodes, this drag and buoyancy is negligible relative to the prevailing forces described above for the lower movement speeds and lower atmospheric pressures. It is apparent that the electric field force to which the particles are subjected is proportional not only to the field strength but also to the amount of charge of the particles, and that the electric field gradient force is only related to the field strength gradient, and that the supporting or recoil force of the electrode depends on whether the electrode is a lower electrode or an upper electrode and the contact speed of the particles with it. Under a direct current field, once the field strength reaches the lifting field strength of the particles (i.e., the electric field force is greater than gravity), the particles will be lifted, accelerated, and "flung" towards the upper electrode. By contact, the particles transfer their charge to the upper electrode and acquire a charge of the same number from the upper electrode, and then are accelerated "back-folded" under the combined action of gravity and electric field forces, and reciprocate between the two electrodes. If the alternating current field is not strong enough, the particles cannot be accelerated from the lower electrode to the upper electrode within half the circumference of the alternating current field, as compared to the case of the direct current field, and the particles will only jump on the lower electrode. That is, the particles are accelerated upward in one half of the alternating field, and are "attracted together" by the electric field force and gravity to the lower electrode in the other half of the alternating field.
The effect of aluminum electrode fluorination on particle movement, whether by a direct current or alternating current electric field, is apparently due to the barrier of the fluorinated layer or fluorinated surface to the transfer of charge between the particles and the electrode. The fluorinated layer reduces the "charge rate" of the charge or charge amount in a given time, so that the particles are subjected to a smaller electric field force in the case of a fluorinated electrode than in the case of a non-fluorinated electrode at the same voltage.
The experiment was performed in an electric field generated by "aluminum ball electrode/aluminum disk electrode" or "stainless steel ball electrode/stainless steel disk electrode" as shown in fig. 2. As shown in FIG. 3, the diameter of the metal ball electrode was phi 4cm, the diameter of the metal disk electrode was phi 10cm, the thickness was 16mm, and the metal disk electrode had rounded edges, the distance between the ball electrode and the disk electrode was 3.2cm, and the metal particles were initially located just under the ball electrode.
Two shapes of metal particles were subjected to a particle lift test, one being "drum metal particles" and the other being "linear metal particles". The drum-shaped metal particles are formed by pressing aluminum ball particles in a stainless steel template by using a flat vulcanizing machine, so that the height of the drum-shaped metal particles is the thickness of the template. For example, a "drum-shaped pellet" having a height of 1.0mm was obtained by pressing an aluminum pellet having a diameter of 1.5mm in a stainless steel form having a thickness of 1.0mm, and was noted as: al-1.5_1. For another example, "drum-shaped particles" having a height of 2.0mm were obtained by pressing aluminum balls having a diameter of 30mm in a stainless steel mold having a thickness of 2.0mm, and were noted as: al-3.0_2. The "linear metal particles" used in the test were stainless steel needles of different diameters and lengths, which were abbreviated as "particle-diameter-length". Stainless steel needles with a diameter of 0.3mm and a length of 5mm will be noted as: "particles-0.3-5".
The boosting mode of the test is similar to the step boosting mode of the epoxy insulation flashover test (shown in fig. 4) whether the test is a particle lift test under a direct current electric field or an alternating current electric field. That is, in the particle lift test, the voltage was first linearly boosted to a certain appropriate level, followed by 1.0kV (or less) per step height, and 2min per step maintenance time, until the particle lift. Before the start of the pressure increase, the experimental atmosphere (0.1 mpa SF6) was allowed to stand for at least 1h, and it was ensured that the particles were located directly under the ball electrode or at the center of the plate electrode. The dc or ac priming test was repeated 10 times or 5 times for each particle, and the average of these several measurements was taken as the dc or ac priming voltage for that particle.
It should be noted that although electrodes of two materials, aluminum and stainless steel, were used for the investigation of the effect of electrode fluorination on the activation of metal particles, only the test results of the "aluminum ball electrode/aluminum disk electrode" combination were given in this report, since similar or similar results were obtained.
(1) Activation of drum-shaped metal particles in an electric field of a fluorinated metal electrode
Tables 1 and 2 summarize the average lift-off voltages of 5 drum aluminum particles in the DC and AC fields generated by unfluorinated or 250 ℃ 50h fluorinated Al ball electrode/disk electrode, respectively. As can be seen from table 1, for each shape and size of drum metal particle, the fluorination of the Al ball electrode/disk electrode significantly increased its dc-onset voltage, with a dc-onset voltage increase of 8.07% to 14.67%, and exhibited some correlation with the particle shape and size. Table 2 shows that the fluoride induced metal particle ac voltage increase is not very significant or as significant as the dc voltage increase compared to the aluminum electrode fluoride induced dc voltage increase. It should be noted, however, that the step height chosen for the boosting process should affect the measurement accuracy, as previously described, where the step height is 1.0kV. In addition, as described below, the rise in the lift-off voltage is closely related to not only the fluorination condition or degree of fluorination of the electrode but also the uniformity of the electric field. For example, drum Al-3.0_2 has an AC lift voltage raised by 11.18% in a uniform field of two aluminum plate electrodes fluorinated at 250 ℃ for 50 hours.
TABLE 1 Al ball electrode/disk electrode DC Start Voltage of Metal particles before and after 50h fluorination at 250 ℃
The particles start to be exposed to an electric field force greater than gravity, and the electric field force to which the particles are exposed is proportional to their charge. The increase in the particle lift voltage caused by electrode fluorination is apparently due to the barrier effect of the fluorinated layer or surface of the electrode to the transfer of charge from the electrode to the metal particles, so that the particles need to be subjected to an electric field force greater than their gravity at higher field strengths or voltages to initiate lift.
TABLE 2 Al ball electrode/disk electrode AC Start Voltage of Metal particles before and after 50h fluorination at 250 ℃
The aluminum electrode fluorination at 250 ℃ for 50 hours is completed in an oil heating small-sized reaction kettle. In order to study the influence of metal electrode fluorination conditions on particle initiation, the section simultaneously studies the metal particle initiation in an electric field of a 300 ℃ 20h fluorinated Al ball electrode/circular plate electrode. This fluorination at higher temperatures is carried out in an electrically heated large reaction vessel as described hereinafter. In order to increase comparability, this section also carried out again a particle lift test in the electric field of the non-aluminum fluoride electrode and the aluminum fluoride electrode at 250 ℃ for 50 hours. These particle lift comparison tests were also performed in a flashover chamber filled with 0.1mpa SF6 gas using the same Al ball electrode/disk electrode combination.
TABLE 3 DC initiation voltages for drum particles in 250℃50h fluorination and 300℃20h fluorination Al ball electrode/disk electrode electric fields
Table 3 shows in comparison the DC lift-off voltages of "drum metal particles" in the electric field of an unfluorinated aluminum electrode, a 250 ℃ 50h aluminum fluoride electrode and a 300 ℃ 20h aluminum fluoride electrode. It can be seen that the results of the particle lift voltage in the electric field of the non-aluminum fluoride electrode and the aluminum fluoride electrode at 250 ℃ for 50 hours are well consistent with the results of the previous test, and the fluorination significantly increases the direct current lift voltage of the "drum-shaped particles", especially in the case of the particles "Al-1.5_1". Comparing the results in the electric field of the 300 ℃ 20h aluminum fluoride electrode with the 250 ℃ 50h aluminum fluoride electrode, it can be seen that the DC voltage is clearly higher than the DC voltage. This further increase in dc-boost voltage with electrode fluorination temperature should be due to the increase in the degree of surface modification of the aluminum electrode (thickness of fluorinated layer and degree of fluorination) with fluorination temperature, although the 300 ℃ fluorination treatment time (20 h) is shorter.
Meanwhile, according to the moving images of the metal particles in the experiment, according to the specific implementation mode of the steps S40-S50, the charging speed of the metal particles in the experiment is calculated, and the fluorination temperature and the duration of the fluorination motor are combined and recorded into a database as fluorination parameters.
The foregoing is merely illustrative of the present invention, and the present invention is not limited thereto, and any person skilled in the art will readily recognize that variations or substitutions are within the scope of the present invention. Therefore, the protection scope of the invention is subject to the protection scope of the claims.
Claims (5)
1. The electrode fluorination evaluation method based on metal particle initiation is characterized by comprising the following steps of:
S10, acquiring a moving image of metal particles in a GIS to be detected;
s20, acquiring a motion trail of the metal particles according to the motion image;
S30, acquiring a plurality of track sections in which metal particles exist in the motion track of the metal particles, contact with electrodes in the GIS and are charged and rebound to the highest point, and marking the track sections as rebound track sections:
1) Analyzing inflection points and extreme value characteristic points of the motion trail curve based on the motion trail curve of the metal particles in the step S20; extracting inflection points appearing in the process of freely falling metal particles and vertexes appearing in the process of rebound rising, and dividing the inflection points into two categories of a falling critical point and a rising critical point;
2) Judging whether each critical point corresponds to collision rebound behavior caused by contact of metal particles with the electrode below the electrode in the GIS according to the coordinate values and the occurrence time of the two types of critical points and by combining the shape characteristics of the motion track curve; analyzing whether the curve trend after descending the boundary points has a corresponding ascending process;
3) For a specific point pair which is judged to be a collision rebound critical point, extracting a curve segment between the specific point pair as a track section of a rebound process; dividing a rebound corresponding part in an original track by adopting curve cutting operation to obtain mathematical representation of a rebound curve segment;
4) Repeating the judging and extracting processes, identifying all rebound curve sections in the whole motion track, wherein each rebound curve section is represented by a descending critical point and an ascending critical point and corresponds to a complete collision rebound behavior; all the obtained rebound curve segments are stored in a centralized and unified way, and extraction and acquisition of rebound track segments are completed;
s40, for the rebound track section, the charging quantity of the metal particles is calculated by combining the gravity of the metal particles and the intensity of the electric field inside the GIS:
1) Reconstructing a three-dimensional geometric model of the interior of the GIS to be detected in finite element analysis software, loading actual electric field control parameters, and performing simulation calculation to obtain an electrostatic field space distribution rule of the interior region of the GIS; establishing a lookup table database of electric field distribution results;
2) Acquiring physical attribute parameters of the metal particles, including data of shape size, material density and volume mass;
3) Extracting the data of each rebound track segment stored in the step S30, wherein the data comprise rebound motion duration, motion starting point and end point coordinates;
4) Setting a kinematic analysis model and a collision dynamics model, and determining the motion speed, the stress, the acceleration and the charge quantity of the metal particles at each moment of rebound motion by adopting a numerical calculation method according to rebound parameters, an electric field distribution lookup table, metal particle attribute parameters and a Newton's law principle;
5) Storing and outputting a data curve or a characteristic value of the electrification quantity of the metal particles in the whole rebound movement process; extracting the maximum charge value of the curve as a final metal particle charge amount calculation result;
S50, calculating the charging speed of the metal particles according to the charge quantity of the metal particles and the contact time of the metal particles with the electrodes in the GIS:
1) Extracting the charging quantity data of the metal particles in the motion of each rebound segment obtained in the step S40;
2) Extracting key characteristic parameters of each rebound section, including rebound duration time and contact duration time data of metal particles and electrodes;
3) Establishing a charge quantity calculation model based on a charge quantity curve; determining the electric charge quantity obtained by the metal particles from the electrode by combining the contact electrode duration parameter and applying a principle rule of conservation of electric charge;
4) Dividing the obtained charge quantity by the contact electrode duration, and calculating the average charge transfer rate, namely the charging speed of the metal particles;
5) Storing charging speed results corresponding to each rebound segment, visually displaying, and drawing a charging speed curve or a statistical histogram;
s60, matching the charging speeds of the metal particles corresponding to the rebound track sections in a preset charging speed-electrode fluorination database to obtain a plurality of electrode fluorination indexes, wherein the electrode fluorination indexes comprise fluorination temperature and fluorination duration:
1) Constructing a correlation database of charging speed-fluorination index according to a motion test of metal particles in a GIS of electrode fluorination; the database reflects the mapping relation of the charging speed and the fluorination index;
2) Obtaining the charging speed results of the metal particles of each rebound section output in the step S50;
3) Searching a matching item closest to the charging speed values in a pre-constructed database, and extracting a corresponding fluorination index, wherein the corresponding fluorination index comprises electrode loss temperature and loss duration indexes measured through experiments;
s70, clustering the obtained electrode fluorination indexes, and taking a clustering center as the fluorination index of the GIS electrode to be detected:
1) Extracting key characteristic parameters from the plurality of matching electrode fluorination index data sets obtained in the step S60, and constructing a characteristic vector by using a principal component analysis method;
2) Grouping the data sets based on the feature vectors of the fluorination indexes by using a K-means or fuzzy C-means clustering algorithm; extracting class center points of all clusters;
3) Comparing electrode fluorination temperatures and time indexes of different clustering center points, and selecting one center point with the largest index value;
4) And taking the fluorination temperature and the fluorination duration of the maximum index center as the lumped results of all rebound sections, and finally outputting the result as the estimated fluorination index of the electrode area of the GIS equipment to be detected.
2. The electrode fluorination evaluation method based on metal particle lift as claimed in claim 1, wherein the step of obtaining a moving image of the metal particles in the GIS to be measured comprises the following steps: the high-speed camera equipment is used for acquiring metal particle moving images and preprocessing, including distortion correction, filtering and enhancement.
3. The method for evaluating electrode fluorination according to claim 1, the method is characterized in that the nearest matching item acquisition method is cosine similarity.
4. A computer readable storage medium, wherein program instructions are stored in the computer readable storage medium, which program instructions, when executed, are adapted to perform a metal particle initiation-based electrode fluorination assessment method of any one of claims 1-3.
5. An electrode fluorination evaluation system based on the activation of metal particles comprising the computer readable storage medium of claim 4.
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