Method for predicting dc surface flashover voltage of insulator of high-voltage gas insulated power transmission equipment

Abstract

The application relates to a high-voltage gas insulated power transmission equipment insulator DC surface flashover voltage prediction method, which comprises the following steps: preparing multiple to-be-tested samples based on a preset insulating material formula to test gas-solid interface characteristics, obtaining a test result, determining multiple input parameters of each to-be-tested sample of the multiple to-be-tested samples, performing statistical analysis on a DC surface flashover voltage as a core parameter, obtaining a correlation coefficient of the multiple input parameters and the DC surface flashover voltage, sorting absolute values of the correlation coefficient in a preset order to obtain multiple corresponding gas-solid interface parameters, and then combining the test result as a training set to train a regression prediction model to predict the DC surface flashover voltage of the material. Therefore, the problems, such as high price of the insulating material, high waste rate, poor recyclability, easy resource waste and environmental pollution, etc. in the related art are solved, the operation stability of the insulating material and the DC surface flashover voltage are improved by testing the gas-solid interface characteristics.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of voltage test, in particular to a DC surface flashover voltage prediction method for insulators of high-voltage gas insulated power transmission equipment. BACKGROUND

[0002] High-voltage gas insulated power transmission equipment (GIS (Gas Insulated Switchgear, DC gas insulated metal enclosed switch) / GIL (Gas Insulated Transmission Line, gas insulated metal enclosed power transmission pipeline)) has the advantages of compact structure, insensitivity to environmental conditions, large transmission capacity, low maintenance requirement, etc., and has been widely used in modern power transmission networks. In recent years, environmentally friendly electrical equipment requires environmental impact assessment throughout the life cycle to reduce environmental pollution of GIS / GIL - from the extraction of raw materials, manufacturing to the regulation and operation of energy and equipment, and until the end of life for GIS / GIL disassembly and material recycling. Therefore, more and more manufacturers have begun to consider the environmental friendliness and recyclability of insulating materials. At present, the support insulators (pot insulators and support insulators) used in GIS / GIL are all made of epoxy resin aluminum oxide composite material system pouring and curing, which has the following problems.

[0003] The epoxy resin system of GIS / GIL support insulators in China generally uses imported materials from abroad. First, foreign countries have a monopoly position in terms of material cost, and the product price is relatively high. Second, due to the stiffness and brittleness of epoxy resin, the thickness and shape of epoxy resin insulating parts are required to be high, and the amount is large. In the production process of insulating parts, due to the inevitable scrap rate, it will result in a large amount of waste. Among them, about 100 million tons of waste are produced in the production process in China every year. Due to the poor recyclability of thermosetting material epoxy resin composite material containing inorganic fillers, at present, these waste products have no special recycling way in the international range, and cannot be reused, but can only be treated by incineration or landfill. In addition, the epoxy-based insulating parts in the retired high-voltage equipment can also be treated by incineration or landfill, which causes serious waste of resources and pollutes the environment. As a recyclable polymer, thermoplastic material completely avoids the large waste and pollution in the production and retirement process of thermosetting material, and has great replacement potential. To find thermoplastic materials used as insulating materials in high-voltage applications, the main characteristics to be met are as follows: (1) good mechanical and electrical properties; (2) good chemical resistance to insulating gas decomposition products; (3) good recyclability; (4) easy to obtain from the market; (5) reliable processing technology.

[0004] As one of the five engineering plastics, PET (Polyethyl Enetereph Thalate) has a highly symmetrical molecular structure. As a semi-crystalline polymer capable of high crystallization, PET material has a smooth surface, excellent anti-creep, anti-mechanical fatigue and friction resistance. The benzene ring structure in the PET molecular chain greatly enhances the material rigidity, making it have excellent material strength. In addition, PET also has excellent electrical insulation performance, and it is feasible to use PET composite material to replace epoxy resin to manufacture GIS / GIL internal insulators.

[0005] Using PET composite material to replace epoxy resin requires research on the electrical properties and insulation failure of PET composite material. The DC surface flashover phenomenon (referred to as DC surface flashover) occurring at the interface between the insulating material and the sulfur hexafluoride gas is a common insulation failure in DC gas insulated metal enclosed switch or gas insulated metal enclosed power transmission pipeline. However, due to the complexity of the DC surface flashover process and the influence of many factors on the gas-solid interface, it brings great difficulty to the analysis of DC flashover. In addition, the DC surface flashover test under the sulfur hexafluoride gas atmosphere requires the use of a large amount of expensive greenhouse gas (i.e. sulfur hexafluoride), and the charging and discharging process makes the cycle of a set of DC surface flashover test extremely long. Therefore, during the improvement of the formula of the insulating material, directly using the DC surface flashover test under the sulfur hexafluoride gas atmosphere to measure the performance is not economical, not environmentally friendly and time-consuming. In addition, few researchers directly explore the correlation between different interface characteristics on the gas-solid interface and the DC surface flashover. Therefore, it is difficult to improve the DC surface flashover voltage of the gas-solid interface of the insulating material in a targeted and directional manner.

[0006] In order to be able to judge the insulation characteristics without performing the DC surface flashover test under the sulfur hexafluoride gas atmosphere, and also to improve the operation stability of the insulating material in a high-voltage environment and improve its DC surface flashover voltage, it is necessary to study a DC surface flashover voltage prediction method based on the gas-solid interface characteristics of the insulating material. SUMMARY

[0007] The present application provides a high-voltage gas insulated power transmission equipment insulator DC surface flashover voltage prediction method to solve the problems of high price, high scrap rate, poor recyclability of the insulating material used in the related art, and easy to cause resource waste, environmental pollution and other problems by incineration or landfill.

[0008] The first aspect of the present application provides a high-voltage gas insulated power transmission equipment insulator DC surface flashover voltage prediction method, comprising the following steps:

[0009] Based on the preset insulating material formula, a plurality of test samples are prepared, and the gas-solid interface characteristics of the plurality of test samples are tested to obtain test results;

[0010] determining a plurality of input parameters of each of a plurality of test samples;

[0011] based on the plurality of input parameters of each of the plurality of test samples, performing statistical analysis with the DC surface flashover voltage as a core parameter to obtain a correlation coefficient between the plurality of input parameters and the DC surface flashover voltage, and sorting absolute values of the correlation coefficient in a preset order to obtain a plurality of gas-solid interface parameters corresponding to a maximum correlation coefficient; and

[0012] using the plurality of gas-solid interface parameters and the test results as a training set to train a regression prediction model to predict the DC surface flashover voltage of the material.

[0013] According to an embodiment of the present application, the gas-solid interface characteristic test is performed on a plurality of test samples to obtain test results, including:

[0014] performing electroluminescence test on the plurality of test samples to obtain surface electroluminescence photon emission quantity of the plurality of test samples;

[0015] performing surface charge dissipation test on the plurality of test samples to obtain surface potential distribution data of the plurality of test samples;

[0016] performing DC surface flashover test on the plurality of test samples to obtain distribution data of surface flashover voltage values of the plurality of test samples;

[0017] obtaining the test results according to the surface electroluminescence photon emission quantity of the plurality of test samples, the surface potential distribution data of the plurality of test samples, and the distribution data of the surface flashover voltage values of the plurality of test samples.

[0018] According to an embodiment of the present application, after obtaining the correlation coefficient between the plurality of input parameters and the DC surface flashover voltage, further comprising:

[0019] obtaining the gas-solid interface characteristic parameters of the target insulating material;

[0020] weighting and summing the gas-solid interface characteristic parameters of the target insulating material and the correlation coefficient to obtain a representation value of the DC surface flashover voltage.

[0021] According to an embodiment of the present application, the input parameters include at least one of surface roughness, water contact angle, surface silicon element atomic ratio, infrared characteristic peak change ratio, steady-state photon number average, steady-state photon number fluctuation amplitude, charge dissipation rate, surface conductivity, dielectric constant, loss tangent, bulk conductivity, bulk conductivity temperature coefficient, and AC breakdown field strength.

[0022] According to an embodiment of the present application, the regression prediction algorithm includes at least one of kernel ridge regression, ridge regression, and support vector machine regression.

[0023] According to one embodiment of the present application, the sample to be tested is a circular piece with a diameter of 10 mm and a thickness of 1 mm, or a square piece with a size of 40 mm*40 mm*2 mm.

[0024] According to the method for predicting the DC surface flashover voltage of the insulator of the high-voltage gas insulated power transmission equipment, a plurality of test samples are prepared based on a preset insulating material formula to test the gas-solid interface characteristics, and test results are obtained. After a plurality of input parameters of each of the plurality of test samples are determined, statistical analysis is performed on the DC surface flashover voltage as a core parameter, a correlation coefficient between the plurality of input parameters and the DC surface flashover voltage is obtained, and the absolute values of the correlation coefficient are sorted in a preset order to obtain a plurality of gas-solid interface parameters. Then, the test results are combined as a training set to train a regression prediction model to predict the DC surface flashover voltage of the material. Thus, the problems of high price, high scrap rate, poor recyclability, and easy resource waste and environmental pollution of the insulating material used in the related art are solved, and the operation stability of the insulating material and the DC surface flashover voltage are improved by testing the gas-solid interface characteristics.

[0025] The second aspect embodiment of the present application provides a device for predicting the DC surface flashover voltage of the insulator of the high-voltage gas insulated power transmission equipment, comprising:

[0026] The test module is configured to prepare a plurality of test samples based on a preset insulating material formula, and test the gas-solid interface characteristics of the plurality of test samples to obtain test results.

[0027] The determination module is configured to determine a plurality of input parameters of each of the plurality of test samples.

[0028] The sorting module is configured to perform statistical analysis on the DC surface flashover voltage as a core parameter based on the plurality of input parameters of each of the plurality of test samples, obtain a correlation coefficient between the plurality of input parameters and the DC surface flashover voltage, and sort the absolute values of the correlation coefficient in a preset order to obtain a plurality of gas-solid interface parameters corresponding to the maximum correlation coefficient.

[0029] The prediction module is configured to use the plurality of gas-solid interface parameters and the test results as a training set to train a regression prediction model to predict the DC surface flashover voltage of the material.

[0030] According to one embodiment of the present application, the test module is specifically configured to:

[0031] The test module is configured to perform electroluminescence testing on the plurality of test samples to obtain the number of surface electroluminescence photon emissions of the plurality of test samples.

[0032] The test module is configured to perform surface charge dissipation testing on the plurality of test samples to obtain surface potential distribution data of the plurality of test samples.

[0033] The DC creeping flashover voltage of the plurality of test samples is obtained by performing DC creeping flashover tests on the plurality of test samples.

[0034] The test result is obtained according to the surface electroluminescence photon emission quantity of the plurality of test samples, the surface potential distribution data of the plurality of test samples, and the distribution data of the DC creeping flashover voltage of the plurality of test samples.

[0035] According to an embodiment of the present application, after the correlation coefficient of the plurality of input parameters and the DC creeping flashover voltage is obtained, the sorting module is further configured to:

[0036] Obtain the gas-solid interface characteristic parameters of the target insulating material.

[0037] The representation value of the DC creeping flashover voltage is obtained by weighted summation of the gas-solid interface characteristic parameters of the target insulating material and the correlation coefficient.

[0038] According to an embodiment of the present application, the input parameters include at least one of surface roughness, water contact angle, surface silicon atomic ratio, infrared characteristic peak change ratio, steady-state photon number average, steady-state photon number fluctuation amplitude, charge dissipation rate, surface conductivity, dielectric constant, loss tangent, bulk conductivity, bulk conductivity temperature coefficient, and AC breakdown field strength.

[0039] According to an embodiment of the present application, the regression prediction algorithm includes at least one of kernel ridge regression, ridge regression, and support vector machine regression.

[0040] According to an embodiment of the present application, the test sample is a round piece with a diameter of 10 mm and a thickness of 1 mm, or a square piece with a size of 40 mm*40 mm*2 mm.

[0041] The DC creeping flashover voltage prediction device for insulators of high-voltage gas insulated transmission equipment according to the embodiments of the present application is based on a plurality of test samples prepared according to a preset insulating material formula for gas-solid interface characteristic testing, obtains a test result, determines a plurality of input parameters of each test sample of the plurality of test samples, performs statistical analysis taking the DC creeping flashover voltage as a core parameter, obtains a correlation coefficient of the plurality of input parameters and the DC creeping flashover voltage, sorts the absolute values of the correlation coefficient according to a preset order, obtains a plurality of corresponding gas-solid interface parameters, and then combines the test result as a training set to train a regression prediction model to predict the DC creeping flashover voltage of the material. Thus, the problems of high price, high scrap rate, poor recyclability, easy resource waste, and environmental pollution of the insulating material used in the related art are solved, the operation stability of the insulating material and the DC creeping flashover voltage thereof are improved by testing the gas-solid interface characteristics.

[0042] The third aspect of the present application provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the DC surface flashover voltage prediction method of the high-voltage gas insulated power transmission equipment insulator as described in the above embodiments.

[0043] The fourth aspect of the present application provides a computer readable storage medium, which stores a computer program executable by a processor to implement the DC surface flashover voltage prediction method of the high-voltage gas insulated power transmission equipment insulator as described in the above embodiments.

[0044] Additional aspects and advantages of the present application will be made apparent by the following description and the accompanying drawings. BRIEF DESCRIPTION OF DRAWINGS

[0045] The above and / or additional aspects and advantages of the present application will become apparent and be readily appreciated from the following description, including the accompanying drawings.

[0046] Figure 1 A flow chart of a DC surface flashover voltage prediction method of a high-voltage gas insulated power transmission equipment insulator according to an embodiment of the present application is provided;

[0047] Figure 2 A schematic diagram of an electroluminescence test platform according to an embodiment of the present application is provided;

[0048] Figure 3 A schematic diagram of a surface charge dissipation experiment platform according to an embodiment of the present application is provided;

[0049] Figure 4 A schematic diagram of a finger electrode structure according to an embodiment of the present application is provided;

[0050] Figure 5 A flow chart of a kernel ridge regression analysis according to an embodiment of the present application is provided;

[0051] Figure 6 A comparison between actual and predicted values of DC surface flashover voltage according to an embodiment of the present application is provided;

[0052] Figure 7 A block diagram of a DC surface flashover voltage prediction device of a high-voltage gas insulated power transmission equipment insulator according to an embodiment of the present application is provided;

[0053] Figure 8 A schematic diagram of an electronic device according to an embodiment of the present application is provided. DETAILED DESCRIPTION

[0054] Embodiments of the present application are described below in detail, examples of which are shown in the accompanying drawings, wherein the same or similar notations represent the same or similar elements or elements having the same or similar functions throughout. The embodiments described below by reference to the drawings are exemplary and are intended to explain the present application, and cannot be understood as limiting the present application.

[0055] A high-voltage gas insulated power transmission equipment insulator DC surface flashover voltage prediction method according to an embodiment of the present application is described below with reference to the accompanying drawings. In view of the problems of high price, high scrap rate, poor recyclability, and easy resource waste and environmental pollution caused by incineration or landfill of the insulating materials used in the related art mentioned above, the present application provides a high-voltage gas insulated power transmission equipment insulator DC surface flashover voltage prediction method. In the method, a plurality of test samples are prepared based on a preset insulating material formula for gas-solid interface property testing, and test results are obtained. After determining a plurality of input parameters of each of the plurality of test samples, statistical analysis is performed on the DC surface flashover voltage as a core parameter, and the correlation coefficients of the plurality of input parameters and the DC surface flashover voltage are obtained. The absolute values of the correlation coefficients are sorted in a preset order to obtain a plurality of gas-solid interface parameters. Then, the test results are combined as a training set to train a regression prediction model to predict the DC surface flashover voltage of the material. Thus, the problems of high price, high scrap rate, poor recyclability, and easy resource waste and environmental pollution caused by incineration or landfill of the insulating materials used in the related art are solved, and the operation stability of the insulating material and its DC surface flashover voltage are improved by testing the gas-solid interface properties.

[0056] Specifically, Figure 1 A flowchart of a high-voltage gas insulated power transmission equipment insulator DC surface flashover voltage prediction method provided by an embodiment of the present application is shown in FIG. 1.

[0057] As Figure 1 shown, the high-voltage gas insulated power transmission equipment insulator DC surface flashover voltage prediction method includes the following steps:

[0058] In step S101, a plurality of test samples are prepared based on a preset insulating material formula, and the plurality of test samples are subjected to gas-solid interface property testing to obtain test results.

[0059] Furthermore, in some embodiments, gas-solid interface characteristics are tested on multiple test samples to obtain test results, including: electroluminescence testing on multiple test samples to obtain the number of electroluminescent photons emitted from the surface of multiple test samples; surface charge dissipation testing on multiple test samples to obtain surface potential distribution data of multiple test samples; DC edge flash testing on multiple test samples to obtain distribution data of edge flash voltage values ​​of multiple test samples; and obtaining test results based on the number of electroluminescent photons emitted from the surface of multiple test samples, the surface potential distribution data of multiple test samples, and the distribution data of edge flash voltage values ​​of multiple test samples.

[0060] Preferably, the pre-set insulating material used in the embodiments of this application can be a PET composite material. Various test samples for testing different gas-solid interface properties are prepared by using a selected PET insulating material formulation. The test samples are either circular pieces with a diameter of 10 mm and a thickness of 1 mm, or thin square pieces of 40 mm * 40 mm * 2 mm.

[0061] Furthermore, in the gas-solid interface characteristic testing process for various test samples in this application embodiment, the gas-solid interface characteristics and testing methods are shown in Table 1:

[0062] Table 1

[0063]

[0064]

[0065] Based on the gas-solid interface characteristics and testing methods shown in the table, the embodiments of this application also need to be tested in the following aspects.

[0066] First, such as Figure 2 As shown, electroluminescence tests were performed on various test samples to obtain the number of electroluminescent photons emitted from their surfaces. The main principle is that luminescence originates from the process by which atoms (or molecules) in an excited state are de-excited by emitting photons; electroluminescence, on the other hand, is the initial excitation of atoms (or molecules) by an electric field. Electroluminescence reflects the strength of charge carrier excitation, transport, and recombination processes under the influence of an electric field, and can be used to study the surface or internal trap properties of materials, as well as charge storage and transport characteristics.

[0067] Specifically, the electroluminescence test needs to be carried out in a dark room, which has a pair of finger electrodes with a distance of 20 mm, a high-voltage direct-current power supply with a voltage of -8 kV and a voltage rising rate of 300 V / s is used as an excitation, and a grounding bus is grounded through a high-frequency current transformer connected with an oscilloscope to ensure that the luminescence does not come from measurable partial discharge. The luminescence is measured by a photon counting device combined with a photon counting probe, and the spectral response range of the photon counting device is 185 nm-850 nm.

[0068] The test process is as follows: a direct current voltage is applied between the electrodes with a distance of 20 mm, and the voltage is raised to -8 kV, and then the voltage is maintained for 300 s, and in the last 2 s, the voltage is reduced to 0. The number of surface electroluminescence photon emissions during the whole process is recorded, and the counting interval is 200 ms (i.e. the total number of photons generated every 200 ms is recorded).

[0069] Secondly, the surface charge dissipation test is carried out on a plurality of test samples to obtain the surface potential distribution data of the plurality of test samples. As shown in Figure 3 The experimental device mainly comprises a high-voltage power supply, a needle-plate electrode, a Kelvin probe, a stepping motor actuator and its drive, an electrometer, a digital oscilloscope and a computer. The sheet-shaped sample is placed in the center of the needle-plate electrode, and the surface charge of the sheet-shaped sample can be applied by corona discharge of the high-voltage needle electrode; the Kelvin probe is controlled by the stepping motor actuator to freely move in two dimensions on the plane. The real-time data measured by the Kelvin probe are transmitted to the digital oscilloscope through the electrometer, and then the data are sampled by the digital oscilloscope and sequentially transmitted to the computer, and finally the data are sorted by the LabVIEW program on the computer to obtain the measured surface potential distribution data.

[0070] Through Figure 3 the experimental platform device, the main test steps are as follows:

[0071] (1) The sheet-shaped sample is placed above the grounded plate electrode, and the potential distribution of the sample surface in the initial state is measured;

[0072] (2) The ambient humidity and temperature are basically unchanged (all tests are carried out in an environment with a temperature of 20±3℃ and a relative humidity of 20±5%), and the potential distribution of the sample surface is measured again after thirty minutes. If the obtained result is the same as that in (1), the next test is carried out, otherwise the sample surface is wiped with alcohol and the above step is re-performed;

[0073] (3) The needle electrode is placed above the center of the sample, and corona discharge is generated by applying a voltage of -7 kV to the needle electrode with a tip diameter of 10 μm for 5 s. After completion, the needle electrode is removed, and a Kelvin probe is used to scan the surface potential once, and the scanning result is recorded as the initial potential (the time interval from the removal of the needle electrode to the start of the scanning of the probe is 10 s).

[0074] (4) Within 1 hour, surface potential scans were performed every 2 minutes using a Kelvin probe, and the scan results were recorded.

[0075] (5) The surface potential results are calculated using the charge reaction algorithm to obtain the surface charge change curve over time.

[0076] Finally, DC edge flash tests were performed on various test samples to obtain the distribution data of the edge flash voltage values ​​for each sample. For example... Figure 4 The diagram shows the finger electrode structure used in this embodiment, with a distance of 20mm between the electrodes. A pressure knob is located below the platform to apply vertical pressure, ensuring close contact between the sample surface and the metal electrodes.

[0077] The DC edge flash test procedure is as follows:

[0078] (1) Wipe the surface of the epoxy sample and electrode to be tested with alcohol;

[0079] (2) Install the sample to be tested and turn the knob to make the sample and the finger electrode contact surface fit tightly;

[0080] (3) Apply a gradually increasing DC voltage to both sides of the finger electrode until flashover occurs (the entire pressurization process is carried out in a sulfur hexafluoride gas atmosphere at 20°C and 1 atmosphere), and record the flashover voltage. Repeat this DC flashover 60 times for each sample to obtain the distribution of flashover voltage values.

[0081] Furthermore, based on the above-mentioned gas-solid interface property tests on various test samples, this application embodiment can also comprehensively evaluate PET composite material samples with several different process formulations, as shown in Tables 2-5. These represent four different experimental groups, each containing multiple samples for comparative analysis. Details are shown in the following tables:

[0082] Table 2

[0083] Sample No. 1 2 3 4 5 6 PET:PBT 10:0 8:2 6:4 4:6 2:8 0:10

[0084] Table 3

[0085] EMA content 0 phr 1.5 phr 3 phr 5 phr Sample No. EMA-0 phr EMA-1.5 phr EMA-3 phr EMA-5 phr

[0086] Table 4

[0087] CXB content 0 phr 3 phr 6 phr 9 phr Sample No. CXB-0 phr CXB-3 phr CXB-6 phr CXB-9 phr

[0088] Table 5

[0089] PS820 content 0 phr 0.2 phr 0.5 phr 1 phr Sample No. PS820-0 phr PS820-0.2 phr PS820-0.5 phr PS820-1 phr

[0090] As shown in Tables 2-5 above, the test groups are respectively GF (Glass Fiber)-reinforced PET / PBT (PolyButylene Terephthalate) composite material test group, EMA (Ethylene Methyl Acrylate)-toughened PET / GF composite material test group, PET / GF composite material test group with added flame retardant, and PET / PBT composite material test group with added transesterification inhibitor. Through comparison and analysis of the test groups, the input parameters of the interface characteristics obtained by various gas-solid interface testing methods are more accurately obtained to facilitate subsequent tests.

[0091] The test results are obtained by the above-mentioned gas-solid interface characteristic tests on the various test samples, and according to the surface electroluminescence photon emission quantity of the various test samples, the surface potential distribution data of the various test samples, and the distribution data of the various test samples along the lightning voltage value.

[0092] In step S102, the input parameters of each of the various test samples are determined.

[0093] Specifically, after obtaining the corresponding test results by the gas-solid interface characteristic tests on the various test samples, the input parameters of each of the various test samples are extracted. The input parameters include at least one of surface roughness, water contact angle, surface silicon atomic ratio, infrared characteristic peak change ratio, steady-state photon number average, steady-state photon number fluctuation amplitude, charge dissipation rate, surface conductivity, dielectric constant, loss tangent, bulk conductivity, bulk conductivity temperature coefficient, and AC breakdown field strength. The bulk conductivity temperature coefficient is the fitted bulk conductivity temperature coefficient, and the larger the value, the more obvious the conductivity change with temperature.

[0094] For example, when extracting the input parameters of each of the various test samples, the surface roughness is obtained by surface topography test, the water contact angle is obtained by water contact test, the surface silicon atomic ratio is obtained by surface chemical composition test, the infrared characteristic peak change ratio is obtained by surface chemical bonding test, the electroluminescence steady-state photon number is obtained by surface trap density test, the surface conductivity is obtained by surface conductivity test, the power frequency dielectric constant and dielectric loss are obtained by dielectric constant and dielectric loss test, and the power frequency partial discharge quantity is obtained by partial discharge test.

[0095] In step S103, based on the plurality of input parameters of each to-be-tested sample piece, statistical analysis is performed on the DC along lightning voltage as a core parameter, a correlation coefficient of the plurality of input parameters and the DC along lightning voltage is obtained, and absolute values of the correlation coefficients are sorted according to a preset order to obtain a plurality of gas-solid interface parameters corresponding to the maximum correlation coefficient.

[0096] Further, in some embodiments, after obtaining the correlation coefficient of the plurality of input parameters and the DC along lightning voltage, the method further includes: obtaining a gas-solid interface characteristic parameter of a target insulating material; and obtaining a representation value of the DC along lightning voltage by weighted summation of the gas-solid interface characteristic parameter of the target insulating material and the correlation coefficient.

[0097] Specifically, according to the plurality of input parameters extracted from each to-be-tested sample piece, statistical analysis is performed on the DC along lightning voltage as a core parameter, a correlation coefficient of each input parameter and the DC along lightning voltage is obtained, and absolute values of the correlation coefficients are sorted according to a preset order to obtain a plurality of gas-solid interface parameters corresponding to the maximum correlation coefficient, that is, a set of target parameters based on the tested insulating material system is obtained. It should be noted that, due to the low regularity of the test data, the Pearson correlation coefficient can be used to describe the relationship between the interface parameters and the DC along lightning voltage. The Pearson correlation coefficients of the gas-solid interface parameters and the DC along lightning voltage obtained by calculation are shown in Table 6:

[0098] Table 6

[0099] Interfacial property Correlation coefficient Amplitude of fluctuation of steady-state photon number -0.80 Mean of steady-state photon number -0.73 Charge dissipation rate 0.33 Dielectric loss 0.25 Surface energy 0.17 Dielectric constant -0.15

[0100] Through analysis of the data in the table, it can be obtained that for the DC along lightning voltage, the absolute value of the correlation coefficient has the following size relationship: the steady-state photon number fluctuation amplitude > the steady-state photon number mean value > the charge dissipation rate > the dielectric loss > the surface energy > the dielectric constant. Among them, the steady-state photon number fluctuation amplitude, the steady-state photon number mean value and the dielectric constant are negatively correlated, and the rest are positively correlated.

[0101] According to the above results, it can be known that under the PET material system, the three key factors affecting the DC along surface flashover are the steady-state photon number fluctuation amplitude, the steady-state photon number mean value and the charge dissipation rate. That is, the lower the steady-state photon number fluctuation amplitude and mean value and the higher the charge dissipation rate, the higher the DC along surface flashover voltage.

[0102] Therefore, the DC along lightning voltage of the epoxy material can be improved by reducing the steady-state photon number fluctuation amplitude and mean value of the PET material and improving the charge dissipation rate.

[0103] Furthermore, in this embodiment, after obtaining the correlation coefficients between multiple input parameters and DC flashover voltage, the DC flashover voltage of insulating materials in the same system can be obtained by measuring their gas-solid interface characteristic parameters and weighted summing them with the correlation coefficients, thus avoiding the need for DC flashover voltage testing. This characteristic value reflects the withstand voltage capability of the insulating material through its magnitude; a larger value indicates a stronger withstand voltage capability.

[0104] In step S104, multiple gas-solid interface parameters and test results are used as a training set to train a regression prediction model to predict the DC surface flashover voltage of the material.

[0105] Specifically, in this embodiment, the test results of multiple gas-solid interface parameters and DC surface flashover voltage obtained from the above tests are used as a training set. Regression prediction algorithms, such as kernel ridge regression, ridge regression, and support vector machine regression, are then used to predict the DC surface flashover voltage of newly acquired materials. The kernel ridge regression analysis process is as follows: Figure 5 As shown, the analysis is carried out through four steps: data standardization, dataset partitioning, training, and prediction and validation, in order to obtain the prediction of the DC surface flashover voltage of the material.

[0106] Furthermore, the comparison between the predicted and actual DC flashover values ​​obtained through analysis in the embodiments of this application is as follows: Figure 6 As shown in the figure, the comparison of data reveals that the predicted DC-line lightning field intensity basically reflects the actual DC-line lightning voltage variation trend. The largest error occurs in the sixth data set, where the predicted value of 64.9 kV is 4.0 kV lower than the actual value of 68.9 kV, resulting in an error rate of 5.8%. The smallest error occurs in the fifth data set, where the predicted value of 69.2 kV is identical to the actual value of 69.2 kV. Therefore, the variation trend of the actual DC-line lightning voltage can be obtained through kernel ridge regression analysis.

[0107] In summary, the DC surface flashover voltage prediction method for GIS / GIL insulators based on gas-solid interface characteristics in the PET composite material system, as described in this application, achieves the following two objectives:

[0108] (1) The test results obtained by this method can improve the gas-solid interface characteristics of the insulating material in a targeted and directional manner, thereby improving the operational stability of the insulating material under high pressure.

[0109] (2) This method is used to predict the change of DC flashover voltage between different insulating material formulations in the PET material system, thereby avoiding the need to conduct DC flashover tests in a sulfur hexafluoride gas atmosphere.

[0110] According to the high-voltage gas insulated power transmission equipment insulator DC surface flashover voltage prediction method of the embodiment of the application, a plurality of to-be-tested samples are prepared based on a preset insulation material formula to perform gas-solid interface characteristic testing, and test results are obtained. After a plurality of input parameters of each to-be-tested sample of the plurality of to-be-tested samples are determined, statistical analysis is performed on the DC surface flashover voltage as a core parameter, a correlation coefficient of the plurality of input parameters and the DC surface flashover voltage is obtained, and absolute values of the correlation coefficients are sorted in a preset order to obtain a plurality of gas-solid interface parameters. Then, the test results are combined as a training set to train a regression prediction model to predict the DC surface flashover voltage of the material. In this way, the problems of high price, high scrap rate, poor recyclability, and easy resource waste and environmental pollution of the insulation material used in the related art are solved, and the operation stability of the insulation material and the DC surface flashover voltage are improved by testing the gas-solid interface characteristics.

[0111] Secondly, the high-voltage gas insulated power transmission equipment insulator DC surface flashover voltage prediction device according to the embodiment of the application is described with reference to the accompanying drawings.

[0112] Figure 7 The high-voltage gas insulated power transmission equipment insulator DC surface flashover voltage prediction device according to the embodiment of the application is a block schematic diagram.

[0113] As shown in Figure 7 The high-voltage gas insulated power transmission equipment insulator DC surface flashover voltage prediction device 10 includes a testing module 100, a determination module 200, a sorting module 300, and a prediction module 400.

[0114] The testing module 100 is configured to prepare a plurality of to-be-tested samples based on a preset insulation material formula, and perform gas-solid interface characteristic testing on the plurality of to-be-tested samples to obtain test results.

[0115] The determination module 200 is configured to determine a plurality of input parameters of each to-be-tested sample of the plurality of to-be-tested samples.

[0116] The sorting module 300 is configured to perform statistical analysis on the DC surface flashover voltage as a core parameter based on the plurality of input parameters of each to-be-tested sample, obtain a correlation coefficient of the plurality of input parameters and the DC surface flashover voltage, sort absolute values of the correlation coefficients in a preset order, and obtain a plurality of gas-solid interface parameters corresponding to the maximum correlation coefficient.

[0117] The prediction module 400 is configured to use the plurality of gas-solid interface parameters and the test results as a training set to train a regression prediction model to predict the DC surface flashover voltage of the material.

[0118] Further, in some embodiments, the testing module 100 is specifically configured to:

[0119] The electroluminescence test is performed on the plurality of test samples to obtain surface electroluminescence photon emission quantity of the plurality of test samples;

[0120] The surface charge dissipation test is performed on the plurality of test samples to obtain surface potential distribution data of the plurality of test samples;

[0121] The DC creepage flashover test is performed on the plurality of test samples to obtain distribution data of the creepage flashover voltage of the plurality of test samples;

[0122] The test result is obtained according to the surface electroluminescence photon emission quantity of the plurality of test samples, the surface potential distribution data of the plurality of test samples, and the distribution data of the creepage flashover voltage of the plurality of test samples.

[0123] Further, in some embodiments, after obtaining the correlation coefficient between the plurality of input parameters and the DC creepage flashover voltage, the ranking module 300 is further configured to:

[0124] Obtain the gas-solid interface characteristic parameters of the target insulating material;

[0125] The representation value of the DC creepage flashover voltage is obtained by weighted summation of the gas-solid interface characteristic parameters of the target insulating material and the correlation coefficient.

[0126] Further, in some embodiments, the input parameters include at least one of surface roughness, water contact angle, surface silicon element atomic ratio, infrared characteristic peak change ratio, steady-state photon number mean value, steady-state photon number fluctuation amplitude, charge dissipation rate, surface conductivity, dielectric constant, loss tangent, bulk conductivity, bulk conductivity temperature coefficient, and AC breakdown field strength.

[0127] Further, in some embodiments, the regression prediction algorithm includes at least one of kernel ridge regression, ridge regression, and support vector machine regression.

[0128] Further, in some embodiments, the test sample is a round piece with a diameter of 10 mm and a thickness of 1 mm, or a square piece with a size of 40 mm*40 mm*2 mm.

[0129] The high-voltage gas insulated power transmission equipment insulator DC surface flashover voltage prediction device according to the embodiment of the application is based on a plurality of to-be-tested samples prepared according to a preset insulating material formula to perform gas-solid interface characteristic testing, to obtain a testing result, after a plurality of input parameters of each to-be-tested sample of the plurality of to-be-tested samples are determined, performing statistical analysis on the DC surface flashover voltage as a core parameter to obtain a correlation coefficient of the plurality of input parameters and the DC surface flashover voltage, and sorting absolute values of the correlation coefficient according to a preset order to obtain a plurality of corresponding gas-solid interface parameters, and then combining the testing result as a training set to train a regression prediction model to predict the DC surface flashover voltage of the material. In this way, the problems of high price, high scrap rate, poor recyclability, easy resource waste, environmental pollution and the like of the insulating material used in the related art are solved, the operation stability of the insulating material and the DC surface flashover voltage thereof are improved by testing the gas-solid interface characteristics.

[0130] Figure 8 The electronic device provided by the embodiment of the application is shown in the structural schematic diagram of the electronic device. The electronic device can include:

[0131] The memory 801, the processor 802 and the computer program stored in the memory 801 and executable on the processor 802.

[0132] The processor 802 implements the high-voltage gas insulated power transmission equipment insulator DC surface flashover voltage prediction method provided in the above embodiment when executing the program.

[0133] Further, the electronic device further includes:

[0134] The communication interface 803 is used for communication between the memory 801 and the processor 802.

[0135] The memory 801 is used to store the computer program executable on the processor 802.

[0136] The memory 801 can include a high-speed RAM memory, and can also include a non-volatile memory, for example, at least one disk memory.

[0137] If the memory 801, the processor 802 and the communication interface 803 are implemented independently, the communication interface 803, the memory 801 and the processor 802 can be connected with each other through a bus and complete communication between each other. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For convenience of representation, Figure 8 Only one thick line is used to represent the bus in the figure, but it does not mean that there is only one bus or only one type of bus.

[0138] Optionally, in a specific implementation, if the memory 801, the processor 802 and the communication interface 803 are integrated on a chip, the memory 801, the processor 802 and the communication interface 803 can complete communication between each other through an internal interface.

[0139] The processor 802 can be a central processing unit (CPU), or an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present application.

[0140] The embodiments of the present application also provide a computer readable storage medium, which stores a computer program, and the program is executed by a processor to implement the high-voltage gas insulated transmission equipment insulator DC surface flashover voltage prediction method as above.

[0141] In the description of the present specification, the description of the terms "one embodiment", "some embodiments", "an example", "a specific example" or "some examples" means that the specific features, structures, materials or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present application. In the present specification, the illustrative description of the above terms is not necessarily for the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any appropriate manner in any one or more embodiments or examples. In addition, the person skilled in the art can combine and combine the different embodiments or examples described in the present specification and the features of the different embodiments or examples without contradiction.

[0142] Although the embodiments of the present application have been shown and described above, it is understood that the above-described embodiments are exemplary and are not to be construed as limiting the present application, and that changes, modifications, substitutions and variations can be made by those skilled in the art without departing from the scope of the present application.

Claims

1. A method for predicting the DC surface flashover voltage of insulators in high-voltage gas-insulated power transmission equipment, characterized in that, Includes the following steps: Based on a pre-defined insulating material formula, various test samples were prepared, and the gas-solid interface characteristics of the various test samples were tested to obtain the test results. The insulating material is a PET composite material; Determine multiple input parameters for each of the various test samples; Based on multiple input parameters of each test sample, statistical analysis is performed with DC flash voltage as the core parameter to obtain the correlation coefficients between multiple input parameters and DC flash voltage. The absolute values ​​of the correlation coefficients are sorted in a preset order to obtain multiple gas-solid interface parameters corresponding to the maximum correlation coefficient. as well as Multiple gas-solid interface parameters and test results were used as a training set to train a regression prediction model to predict the DC surface flashover voltage of the material. Gas-solid interface properties were tested on various test samples, and the test results were obtained, including: Electroluminescence tests were performed on various test samples to obtain the number of electroluminescent photons emitted from the surface of each test sample. Surface charge dissipation tests were performed on various test samples to obtain surface potential distribution data for the various test samples. DC edge flash tests were performed on various test samples to obtain the distribution data of the edge flash voltage values ​​of various test samples; The test results were obtained based on the number of surface electroluminescent photons emitted by various test samples, the surface potential distribution data of various test samples, and the distribution data of the flashover voltage values ​​of various test samples.

2. The method according to claim 1, characterized in that, After obtaining the correlation coefficients between multiple input parameters and DC flicker voltage, the following is also included: Obtain the gas-solid interface characteristic parameters of the target insulating material; The characterization value of DC flashover voltage is obtained by weighted summation of the gas-solid interface characteristic parameters and correlation coefficients of the target insulating material.

3. The method according to claim 1, characterized in that, The input parameters include at least one of the following: surface roughness, water contact angle, surface silicon atomic ratio, infrared characteristic peak variation ratio, steady-state photon number mean, steady-state photon number fluctuation amplitude, charge dissipation rate, surface conductivity, dielectric constant, loss tangent, bulk conductivity, bulk conductivity temperature coefficient, and AC breakdown field strength.

4. The method according to claim 1, characterized in that, Regression prediction algorithms include at least one of kernel ridge regression, ridge regression, and support vector machine regression.

5. The method according to any one of claims 1-4, characterized in that, The test sample is a circular piece with a diameter of 10mm and a thickness of 1mm, or a square piece with a diameter of 40mm*40mm*2mm.

6. A device for predicting the DC surface flashover voltage of insulators in high-voltage gas-insulated power transmission equipment, characterized in that, include: The testing module is used to prepare various test samples based on a preset insulation material formula, and to test the gas-solid interface characteristics of the various test samples to obtain the test results. The insulating material is a PET composite material; The determination module is used to determine multiple input parameters for each of the various test samples. The sorting module is used to perform statistical analysis based on multiple input parameters of each test sample, with DC flash voltage as the core parameter, to obtain the correlation coefficients between multiple input parameters and DC flash voltage, and sort the absolute values ​​of the correlation coefficients according to a preset order to obtain multiple gas-solid interface parameters corresponding to the maximum correlation coefficient. as well as The prediction module is used to train a regression prediction model by using multiple gas-solid interface parameters and test results as a training set to predict the DC surface flashover voltage of the material. The test module is specifically used for: Electroluminescence tests were performed on various test samples to obtain the number of electroluminescent photons emitted from the surface of each test sample. Surface charge dissipation tests were performed on various test samples to obtain surface potential distribution data for the various test samples. DC edge flash tests were performed on various test samples to obtain the distribution data of the edge flash voltage values ​​of various test samples; The test results were obtained based on the number of surface electroluminescent photons emitted by various test samples, the surface potential distribution data of various test samples, and the distribution data of the flashover voltage values ​​of various test samples.

7. The apparatus according to claim 6, characterized in that, After obtaining the correlation coefficients between multiple input parameters and DC flicker voltage, the sorting module is further configured to: Obtain the gas-solid interface characteristic parameters of the target insulating material; The characterization value of DC flashover voltage is obtained by weighted summation of the gas-solid interface characteristic parameters and correlation coefficients of the target insulating material.

8. The apparatus according to claim 6, characterized in that, The input parameters include at least one of the following: surface roughness, water contact angle, surface silicon atomic ratio, infrared characteristic peak variation ratio, steady-state photon number mean, steady-state photon number fluctuation amplitude, charge dissipation rate, surface conductivity, dielectric constant, loss tangent, bulk conductivity, bulk conductivity temperature coefficient, and AC breakdown field strength.

9. The apparatus according to claim 6, characterized in that, Regression prediction algorithms include at least one of kernel ridge regression, ridge regression, and support vector machine regression.

10. The apparatus according to any one of claims 6-9, characterized in that, The test sample is a circular piece with a diameter of 10mm and a thickness of 1mm, or a square piece with a diameter of 40mm*40mm*2mm.

11. An electronic device, characterized in that, include: The method includes a memory, a processor, and a computer program stored in the memory and executable on the processor, the processor executing the program to implement the DC surface flashover voltage prediction method for insulators of high-voltage gas-insulated transmission equipment as described in any one of claims 1-5.

12. A computer-readable storage medium having a computer program stored thereon, characterized in that, The program is executed by a processor to implement the DC surface flashover voltage prediction method for insulators of high-voltage gas-insulated transmission equipment as described in any one of claims 1-5.

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