Preparation method of material compatibility type intelligent early warning insulated bus

Abstract

The invention relates to the technical field of power transmission equipment, discloses a preparation method of a material compatibility type intelligent early-warning insulated bus, and aims to solve the problems that fault monitoring of an existing insulated bus depends on an external sensor, only a single parameter can be detected, the risk of missing report exists, the insulation aging trend pre-judgment capability is lacked, and the reliability is poor. The technical problem that potential risks cannot be avoided in advance in the prior art is solved, an insulating base material, a functional sensing material, a synergistic dispersant and a functional additive are selected according to a preset ratio, a pre-dispersion liquid is prepared after classification pretreatment, then the pre-dispersion liquid and a pre-molten insulating base material are blended, in-situ dispersion and cross-linking reaction are synchronously executed, an insulating layer is formed by adopting a temperature gradient coating process, and the insulating layer is prepared. And finally, an electrode and an early warning module are integrated, a finished product is obtained through constant-temperature maintenance and performance calibration, the sensing material cooperatively responds to different faults, full-fault coverage is achieved, and the method guarantees stable material compatibility, accurate finished product early warning, rapid response, adaptation to multiple application scenes and remarkable industrial application value.

Description

Technical Field

[0001] This invention relates to the field of power transmission equipment technology, and in particular to a method for preparing a material-compatible intelligent early warning insulated busbar. Background Technology

[0002] As a core component of power transmission systems, insulated busbars are widely used in various scenarios such as high-voltage switchgear, new energy power plants, urban substations, power distribution systems of industrial plants, and power supply systems of data center computer rooms, thanks to their core advantages of high-efficiency conductivity and reliable insulation. With the rapid development of the power industry towards intelligence and high reliability, the comprehensive performance requirements of insulated busbars continue to increase, especially the requirements for fault monitoring and risk warning capabilities.

[0003] In the existing technology, the material systems and preparation methods of insulated busbars have formed a diversified development pattern. The mainstream insulating materials include polypropylene, epoxy resin, silicone rubber, cross-linked polyethylene, etc. For example, CN120888139A discloses an insulating tubular busbar material, its preparation method and application, which uses ternary copolymer polypropylene and a specific reinforcing resin as the main resin composition, combined with nanofillers, functional composite compositions and other components to improve the insulation, thermal stability and mechanical properties of the material. CN114283999A discloses a manufacturing method of medium-voltage resin-cast insulated busbars, which adopts a modified epoxy resin casting process and improves the thermal conductivity, toughness and high temperature resistance of epoxy resin by introducing p-toluenesulfonamide-based nano-scandium silicate / yttrium silicate modified components, thereby reducing the coefficient of thermal expansion.

[0004] However, in terms of fault monitoring and risk warning, existing solutions mostly use external temperature sensors, vibration sensors and other components to detect single parameters. This makes it difficult to simultaneously identify multiple core faults such as insulation aging and mechanical damage, resulting in significant potential for missed detections. Furthermore, the detection mode is limited to passive alarms after parameters exceed the limits, lacking the ability to quantitatively predict insulation aging trends. It is also unable to combine key influencing factors such as ambient temperature and humidity to proactively avoid potential risks such as short circuits and fires, ultimately leading to increased operational risks and maintenance costs in the power system. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to overcome the shortcomings of the existing technology. To this end, a method for preparing a material-compatible intelligent early warning insulated busbar is proposed.

[0006] To achieve the above objectives, this application adopts the following technical solution: a method for preparing a material-compatible intelligent early warning insulated busbar, comprising the following steps: S1: preparing an insulating substrate, sensing material, synergistic dispersant, and functional additives according to a preset ratio, and completing classification pretreatment; S2: mixing the sensing material and the synergistic dispersant to prepare a pre-dispersion liquid; S3: blending the pre-dispersion liquid with a pre-molten insulating substrate, simultaneously performing in-situ dispersion and cross-linking reaction of the insulating substrate to prepare a composite melt; S4: using a gradient temperature-controlled coating process, coating the composite melt onto the surface of the pretreated conductive substrate to form an insulating layer; S5: integrating electrodes and an early warning module on the surface of the insulating layer, and obtaining a material-compatible intelligent early warning insulated busbar after constant temperature curing and performance calibration.

[0007] Preferably, in S1, the sensing material includes a temperature sensing material, an insulation aging sensing material, and a mechanical damage sensing material.

[0008] Preferably, the temperature sensing material is selected from one or more of carbon nanotubes, graphene, and ceramic-based negative temperature coefficient thermistor powder.

[0009] Preferably, the insulating aging sensing material is selected from one or more of polyaniline, polypyrrole, and acetylene black.

[0010] Preferably, the mechanical damage sensing material is selected from one or more of carbon fiber, stainless steel conductive fiber, and amine-modified graphene oxide.

[0011] Preferably, in S2, the synergistic dispersant is selected from one or a combination of two of silane coupling agents KH-550 and KH-560, and the total mass of the synergistic dispersant is not less than 50% of the total mass of the sensing material.

[0012] Preferably, in S3, when simultaneously performing the in-situ dispersion and crosslinking reaction with the insulating substrate, a crosslinking agent needs to be added, and the crosslinking agent is selected from dicumyl peroxide.

[0013] Preferably, in S1, the insulating substrate is polyethylene, and the functional additives are composed of nano-silica and UV-resistant carbon black.

[0014] Preferably, in S4, during the gradient temperature control coating process, the coating mold is divided into an inlet section, a middle section, and an outlet section along the direction of travel of the conductive substrate, and the temperatures of the three sections are set to 155-160℃, 145-150℃, and 135-140℃, respectively.

[0015] A material-compatible intelligent early warning insulated busbar is prepared using the same method as an insulated busbar.

[0016] The technical effects and advantages of this invention are as follows:

[0017] In this invention, by combining the functions of temperature sensing materials, insulation aging sensing materials, and mechanical damage sensing materials, and utilizing the differentiated response mechanisms of these three types of sensing materials based on their resistance and temperature sensitivity characteristics, dielectric constant and aging degree correlation characteristics, and conductive network and structural integrity correlation characteristics, a comprehensive coverage of core faults in power equipment is achieved. Simultaneously, a step-by-step progressive process supports the material combination: pretreatment to eliminate interference, pre-dispersion liquid preparation to achieve initial uniformity, in-situ dispersion and synchronous cross-linking to achieve stable bonding, and temperature gradient coating to enhance interface adaptation. This forms a closed-loop design based on synergistic material functions and supported by process assurance, providing key technical support for the intelligent and highly reliable operation of power transmission systems. Attached Figure Description

[0018] The disclosure of this invention is illustrated with reference to the accompanying drawings. It should be understood that the drawings are for illustrative purposes only and are not intended to limit the scope of protection of this invention. In the drawings, the same reference numerals are used to refer to the same parts:

[0019] Figure 1 This is a flowchart illustrating the preparation process of the insulated busbar of the present invention. Detailed Implementation

[0020] It is readily understood that, based on the technical solution of this invention, those skilled in the art can propose various interchangeable structural methods and implementations without altering the essential spirit of the invention. Therefore, the following detailed embodiments and accompanying drawings are merely illustrative examples of the technical solution of this invention and should not be considered as the entirety of the invention or as limitations or restrictions on the technical solution of this invention.

[0021] refer to Figure 1 As shown, the present invention provides a method for preparing a material-compatible intelligent early warning insulated busbar, comprising the following steps:

[0022] S1: Prepare insulating substrate, sensing material, synergistic dispersant and functional additives according to the preset ratio, and complete the classification pretreatment;

[0023] S2: Mix the three types of sensing materials with a synergistic dispersant to prepare a pre-dispersion liquid, and control the agglomeration particle size of the sensing materials to ≤100nm;

[0024] S3: The pre-dispersed liquid is blended with the pre-molten insulating substrate, and in-situ dispersion and cross-linking reaction of the insulating substrate are performed simultaneously to prepare a composite melt;

[0025] S4: Using a gradient temperature-controlled coating process, the composite melt is coated onto the surface of the pretreated conductive substrate to form an insulating layer;

[0026] S5: Electrodes and early warning modules are integrated on the surface of the insulation layer. After constant temperature curing and performance calibration, a material-compatible intelligent early warning insulation busbar is produced.

[0027] According to embodiments of this application, the insulating substrate is selected from industrial-grade polyethylene with a melt flow rate of 0.5-1.0 g / 10 min and a density of 0.92-0.94 g / cm³. 3 ;

[0028] The insulating substrate accounts for 90-92% of the total content. As the supporting framework of the sensing material, it needs to have good melt processability and cross-linking reactivity to provide stable support for the sensing network and ensure the insulation requirements under 1-10kV voltage scenarios.

[0029] The sensing materials can be categorized into temperature sensing materials, insulation aging sensing materials, and mechanical damage sensing materials based on the detection type, accounting for 3-4.5% of the total weight, with each single type accounting for 1-1.5%, to avoid excessive use affecting insulation performance.

[0030] The temperature sensing material is selected from one or more of carbon nanotubes, graphene, and ceramic-based negative temperature coefficient thermistor powder. The common feature of these materials is that the resistance changes regularly with temperature. Among them, carbon nanotubes and graphene have high sensitivity, ceramic-based negative temperature coefficient thermistor powder has strong stability, and conductive carbon black has significant cost advantages. The material can be flexibly selected according to the needs of the application scenario.

[0031] The insulation aging sensing material is selected from one or more of polyaniline, polypyrrole, and acetylene black. During the aging process of the insulation layer, the penetration of the environmental medium will cause changes in the molecular structure or dispersion state of these materials, thereby triggering identifiable fluctuations in dielectric constant or conductivity, and realizing the capture of aging signals.

[0032] The mechanical damage sensing material is selected from one or more of carbon fiber, stainless steel conductive fiber, and amine-modified graphene oxide. These materials form a continuous conductive network inside the insulating layer. When mechanical damage occurs in the insulating layer, the conductive network breaks, the resistance changes suddenly or the capacitance changes, and a damage warning signal is generated.

[0033] The synergistic dispersant is selected from one or a combination of two of silane coupling agents KH-550 and KH-560, accounting for 2-2.5% by weight, and the total mass of the synergistic dispersant is not less than 50% of the total mass of the sensing material; the siloxane groups in the molecule can form chemical bonds with the hydroxyl groups on the surface of the sensing material and the polyethylene molecular chains, which not only prevents the sensing material from agglomerating, but also strengthens the interfacial bonding between materials, avoiding detachment during use and signal failure.

[0034] The functional additives consist of nano-silica and UV-resistant carbon black, accounting for 1-1.5% of the total weight. Nano-silica enhances the mechanical strength of the insulation layer and prevents vibration from triggering damage warnings, while UV-resistant carbon black slows down the aging of the insulation layer itself and reduces interference from non-faulty signals.

[0035] The crosslinking agent used is dicumyl peroxide, with a weight ratio of 0.2-0.3%. At the blending temperature, it decomposes to generate free radicals, initiating crosslinking of the polyethylene molecular chains to form a three-dimensional network structure. This anchors the sensing material within the framework, preventing migration of the sensing material due to thermal cycling and ensuring long-term stability of the warning signal.

[0036] The pretreatment of the insulating substrate includes: placing polyethylene granules in a vacuum drying oven and drying them at 80°C for 3 hours, with the moisture content determined by gas chromatography to be ≤0.03%.

[0037] Moisture is the main cause of bubble generation during the blending process, and bubbles can disrupt the continuity of the sensor network, leading to local signal blind spots. Therefore, drying is a prerequisite for ensuring uniform material compatibility.

[0038] The pretreatment of the sensing materials includes: mixing the sensing materials and then putting them into a high-speed pulverizer and pre-dispersing them at 2000 r / min for 3 min; various sensing materials are prone to forming initial agglomerates during storage, especially fibrous and sheet materials. Pre-dispersing can break the initial agglomeration state and lay the foundation for subsequent uniform dispersion.

[0039] The dispersant pretreatment includes: taking the dispersant according to the specified ratio, adding 3% anhydrous ethanol (by mass), and stirring at 500 rpm for 5 minutes until completely dissolved; after dissolution, the dispersant forms a homogeneous solution, ensuring uniform coating of the sensing material surface and providing uniform reaction sites for subsequent chemical bonding.

[0040] In the specific pre-dispersion preparation process, the pretreated mixed sensing material is added to the dispersant solution and stirred in a star stirrer at 800 r / min for 15 min. Then, it is continuously ultrasonicated at 400W power for 20 min using an ultrasonic disperser. During the ultrasonication process, the system temperature is controlled to be ≤55℃ by water bath cooling. The particle size distribution of the sensing material in the pre-dispersion is ≤100nm and the particle size distribution span is ≤0.3, as detected by a laser particle size analyzer.

[0041] Stirring achieves macroscopic uniform distribution of the sensing material in the dispersant solution, while ultrasound breaks up fine agglomerates through microjets and shock waves generated by cavitation effect, achieving microscopic uniform dispersion. Particle size control of ≤100nm ensures that the sensing material forms a continuous network of interconnected points in the insulating substrate, avoiding signal interruptions caused by excessively large particle size. Water bath temperature control prevents structural damage to materials such as polyaniline and graphene due to overheating, ensuring their functional stability.

[0042] In the in-situ dispersion and synchronous crosslinking process, polyethylene particles are added to a twin-screw extruder. The temperature of the first section of the barrel is set to 140℃, the second section to 145℃, and the third section to 150℃. The mixture is pre-melted at 600 r / min for 3 min. The pre-dispersed liquid and dicumyl peroxide crosslinking agent are added at once. The temperature of the first section of the barrel is adjusted to 150℃, the second section to 155℃, and the third section to 160℃. The screw speed is increased to 800 r / min, and the mixture is blended for 6 min. The vacuum degassing system is turned on with a vacuum degree of -0.09 MPa for 10 min. The melt is then filtered through a 200-mesh single-layer filter.

[0043] The pre-melted polyethylene is in a low-viscosity melt state. At this time, the pre-dispersant is added, and the dispersant can guide the sensing material to diffuse in situ in the melt, achieving uniform mixing at the physical level. At the same time, the temperature range of 150-160℃ just meets the decomposition requirements of dicumyl peroxide. Free radicals initiate the cross-linking of polyethylene to form a three-dimensional skeleton, and the sensing material is locked in the pores of the skeleton, achieving stable bonding at the chemical level. The degassing and filtration steps remove bubbles and impurities, avoiding weak points in insulation and signal interference points.

[0044] The conductive substrate is made of T2 copper or 6063 aluminum alloy. After processing, it is sandblasted with white corundum sand to control the surface roughness Ra to 1.5-2.0μm. Then it is placed in anhydrous ethanol and ultrasonically cleaned with 300W power for 4-6 minutes, and dried at 60-70℃ for 13-17 minutes. The temperature of the coating mold is set in a gradient of 155-160℃ for the inlet section, 145-150℃ for the middle section, and 135-140℃ for the outlet section. The conductive substrate passes through the mold at a uniform speed of 1.0-1.2m / min. The composite melt is coated on the surface of the substrate with a coating thickness of 4-6mm, and then water-cooled for shaping.

[0045] Sandblasting increases the surface roughness of the conductive substrate, expanding the contact area; ultrasonic cleaning removes surface oil and oxide layers, enhancing surface activity; a temperature gradient creates a slow cooling zone at the interface between the composite melt and the conductive substrate, causing a slight chemical reaction between the polyethylene molecular chains in the composite melt and the activated metal atoms on the substrate surface, forming a 10-20μm gradient transition layer, avoiding the peeling problem caused by traditional room temperature coating which is merely physical bonding; slow cooling reduces interfacial temperature stress, further enhancing interfacial stability and ensuring that the insulation layer does not peel off or crack during long-term use, preventing interfacial problems from interfering with sensor signal transmission.

[0046] Then, conductive silver paste was printed on the surface of the insulating layer using a screen printing process, with an electrode spacing of 25mm and a line width of 0.8mm. After drying at 120℃ for 20 minutes, the electrode conductivity was tested with a multimeter and found to be ≤10kΩ. The electrodes are the conductive paths for the sensor network and the early warning module. Insufficient conductivity will lead to signal attenuation. Therefore, it is necessary to strictly control the drying temperature and time to ensure that the conductive silver paste is fully cured.

[0047] The early warning module is embedded in the pre-reserved groove of the insulation layer and sealed with silicone potting compound. The potting compound covers the module surface with a thickness of ≥2mm. The thermal expansion coefficient of the silicone potting compound is compatible with that of cross-linked polyethylene, which can avoid interface peeling caused by temperature cycling. At the same time, its high dielectric strength will not affect the insulation performance.

[0048] The finished product is placed in a constant temperature oven and kept at 80℃ for 24 hours to eliminate the internal stress generated during the preparation process and to prevent the insulation layer from deforming and causing the sensor network to shift.

[0049] This application also provides a material-compatible intelligent early warning insulated busbar, prepared by the above-described method. This insulated busbar can be further applied to power transmission scenarios in high-voltage switchgear, new energy power plants, urban substations, and industrial plant power distribution systems. This application also provides an intelligent power transmission system including the aforementioned material-compatible intelligent early warning insulated busbar.

[0050] The implementation scheme of this application will be described in detail below with reference to specific embodiments. However, those skilled in the art will understand that the following embodiments are only used to illustrate this application and should not be regarded as limiting the scope of this application. Where specific conditions are not specified in the embodiments, they shall be carried out according to conventional conditions or conditions recommended by the manufacturer. Where the manufacturers of the reagents or instruments used are not specified, they are all conventional products that can be purchased commercially.

[0051] Example 1

[0052] This embodiment provides a method for preparing a material-compatible intelligent early warning insulated busbar, which specifically includes the following steps:

[0053] By weight, weigh out 90 parts of polyethylene, 1.2 parts of carbon nanotubes, 1.0 part of polyaniline, 1.0 part of carbon fiber, 2.5 parts of silane coupling agent KH-550, 0.6 parts of nano silica, 0.7 parts of UV-resistant carbon black, and 0.3 parts of dicumyl peroxide.

[0054] The polyethylene particles were dried at 80℃ for 3 hours, and the moisture content was found to be 0.02% by gas chromatography. After mixing carbon nanotubes, polyaniline and carbon fibers, they were placed in a high-speed pulverizer and pre-dispersed at 2000 r / min for 3 minutes. Silane coupling agent KH-550 was added to 3% anhydrous ethanol and stirred at 500 r / min for 5 minutes until completely dissolved.

[0055] The mixed sensing material was added to the dispersant solution and stirred at 800 r / min for 15 min with a star stirrer, followed by ultrasonic dispersion at 400 W for 20 min. The temperature was controlled at 50℃ by water bath cooling. The particle size distribution of the sensing material was measured to be 85 nm by a laser particle size analyzer, with a distribution span of 0.25.

[0056] Polyethylene is added to a twin-screw extruder and pre-melted at 600 r / min for 3 min. Then, pre-dispersed liquid and dicumyl peroxide are added at one time and mixed at 800 r / min for 6 min. After vacuum degassing, the mixture is filtered through a 200-mesh filter to obtain a composite melt.

[0057] The T2 copper conductive substrate is processed into a 40mm×12mm cross section. After sandblasting, Ra=1.8μm. It is ultrasonically cleaned with anhydrous ethanol at 300W for 5 minutes and dried at 60℃ for 15 minutes. The temperature of the coating mold is set in a gradient of 155℃ for the inlet section, 145℃ for the middle section, and 135℃ for the outlet section. The conductive substrate passes through the mold at a uniform speed of 1.1m / min. The composite melt is coated on the surface of the substrate with a coating thickness of 6mm and then water-cooled for shaping.

[0058] The conductive silver paste electrode was screen-printed, dried at 120℃ for 20 minutes, and its conductivity was tested at 7kΩ with a multimeter. The early warning module was encapsulated with silicone potting compound, with a peel strength of 1.5N / mm, and cured at 80℃ for 24 hours to obtain the finished product.

[0059] Example 2

[0060] This embodiment provides a method for preparing a material-compatible intelligent early warning insulated busbar. By weight, 91.5 parts of polyethylene, 1.0 part of conductive carbon black, 1.5 parts of acetylene black, 1.3 parts of metallic copper powder, 2.1 parts of silane coupling agent KH-560, 0.6 parts of nano-silica, 0.6 parts of UV-resistant carbon black, and 0.2 parts of dicumyl peroxide are weighed.

[0061] Example 3

[0062] This embodiment provides a method for preparing a material-compatible intelligent early warning insulated busbar. By weight, 91 parts of polyethylene, 1.5 parts of graphene, 1.2 parts of polypyrrole, 1.5 parts of stainless steel conductive fiber, 2.0 parts of silane coupling agent KH-560, 0.5 parts of nano-silica, 0.5 parts of UV-resistant carbon black, and 0.2 parts of dicumyl peroxide are weighed. The coating thickness is adjusted to 4 mm, the substrate passing speed is 1.2 m / min, and the water cooling and shaping water temperature is 35℃.

[0063] Example 4

[0064] This embodiment provides a method for preparing a material-compatible intelligent early warning insulated busbar. By weight, the following components are weighed: 90.5 parts of polyethylene, 1.3 parts of ceramic-based negative temperature coefficient thermistor powder, 1.1 parts of graphene oxide, 1.2 parts of carbon fiber, 2.2 parts of silane coupling agent KH-550, 0.7 parts of nano-silica, 0.8 parts of UV-resistant carbon black, and 0.25 parts of dicumyl peroxide.

[0065] Example 5

[0066] This embodiment provides a method for preparing a material-compatible intelligent early warning insulated busbar. By weight, 90.8 parts of polyethylene, 1.1 parts of carbon nanotubes, 1.3 parts of polypyrrole, 1.4 parts of stainless steel conductive fiber, 2.3 parts of silane coupling agent KH-550, 0.6 parts of nano-silica, 0.7 parts of UV-resistant carbon black, and 0.2 parts of dicumyl peroxide are weighed.

[0067] Comparative Example 1

[0068] This comparative example provides a method for preparing a material-compatible intelligent early warning insulated busbar. The difference from Example 1 is that: first, polyethylene and dicumyl peroxide are added to a twin-screw extruder and crosslinked at 155°C for 5 min to obtain a crosslinked polyethylene melt; then, a pre-dispersion liquid of the sensing material is added and mixed at 800 r / min for 6 min, thus failing to achieve simultaneous in-situ dispersion and crosslinking.

[0069] Comparative Example 2

[0070] This comparative example provides a method for preparing a material-compatible intelligent early warning insulated busbar. The difference from Example 1 is that the undispersed mixed sensing material, dispersant and polyethylene are directly blended without preparing a pre-dispersed liquid.

[0071] Comparative Example 3

[0072] This comparative example provides a method for preparing a material-compatible intelligent early warning insulated busbar. The difference between this example and Example 1 is that only carbon nanotubes are selected as the sole sensing material, and no polyaniline or carbon fiber is added.

[0073] Comparative Example 4

[0074] This comparative example provides a method for preparing a material-compatible intelligent early warning insulated busbar, which differs from Example 1 in that the proportion of synergistic dispersant is 1%.

[0075] The finished products of Examples 1-5 and Comparative Examples 1-4 were subjected to uniform performance testing. The testing standards, modules and results are as follows to ensure the accuracy, comparability and repeatability of the data.

[0076] Early warning accuracy: Simulate 100 faults, with temperature fluctuations of 3-8℃, insulation aging of 5-30%, and damage of 0.3-1.0mm, and count the number of accurate early warnings;

[0077] Sensing response time: Record the time from the occurrence of the fault to the issuance of the warning signal, and take the average of 5 tests;

[0078] Signal identification: Evaluated by signal-to-noise ratio (SNR), which is the ratio of the fault signal to the environmental interference signal. A signal with an SNR ≥ 3 is considered valid.

[0079] The specific test results are shown in Table 1 below:

[0080] Group Early warning accuracy rate (%) Sensing response time (s) Signal identification Example 1 98 25 4.2 Example 2 95 30 3.5 Example 3 96 28 3.8 Example 4 97 26 4.0 Example 5 94 27 3.7 Comparative Example 1 94 42 3.6 Comparative Example 2 81 38 2.8 Comparative Example 3 72 27 3.6 Comparative Example 4 85 35 3.0

[0081] Table 1

[0082] Dielectric strength: Tested according to GB / T 1408.1-2016 "Electrical strength test method for insulating materials - Part 1: Test at power frequency", with an electrode spacing of 2mm, a voltage rise rate of 2kV / s, and the average value of 10 tests.

[0083] Batch variation: Five groups of samples from the same batch were selected, and their dielectric strength and sensing response time were tested. The coefficient of variation was then calculated.

[0084] Long-term stability: According to GB / T 2423.22-2012 "Environmental testing - Part 2: Test methods - N: Temperature change", the sensor was kept at -40℃ for 2 hours, then heated to 150℃ and kept at 150℃ for 2 hours, and the cycle was repeated 5 times. The change in sensor response time before and after the cycle was tested.

[0085] The test results are shown in Table 2 below:

[0086] Group Dielectric strength (kV / mm) Fluctuation (%) within the same batch Long-term stability (%) Example 1 24.2 ±3.0 95 Example 2 22.0 ±3.8 92 Example 3 22.5 ±3.5 93 Example 4 23.8 ±3.2 94 Example 5 23.0 ±3.4 91 Comparative Example 1 18.5 ±8.5 78 Comparative Example 2 19.8 ±9.2 75 Comparative Example 3 23.5 ±3.3 91 Comparative Example 4 19.2 ±7.5 80

[0087] Table 2

[0088] Test data shows that Examples 1-5 all achieved a warning accuracy rate of 94%-98%, a sensing response time of ≤28s, and a signal recognition rate of ≥3.5, comprehensively covering the three core faults in the operation of power equipment: abnormal temperature, insulation aging, and mechanical damage. In contrast, Example 3 only used a single carbon nanotube as the sensing material, failing to form a complete functional combination. Its warning accuracy rate was only 72%, and it could only respond to temperature signals, unable to identify insulation aging and mechanical damage faults. Compared with Example 1, the warning function loss rate reached 36.7%.

[0089] The signal generation mechanisms of the three types of faults—temperature, insulation aging, and mechanical damage—are completely different, requiring targeted material responses. Temperature faults rely on the material's resistance-temperature sensitivity characteristics, insulation aging faults rely on the material's dielectric constant-aging degree correlation characteristics, and mechanical damage faults rely on the material's conductive network-structural integrity correlation characteristics.

[0090] Compared to Example 1, the dielectric strength dropped to 18.5 kV / mm, the performance fluctuation of the same batch increased to ±8.5%, and the long-term stability was only 78%. The fundamental reason is that the viscosity of the cross-linked polyethylene melt increased significantly, making it difficult for the sensing material to diffuse uniformly and forming local agglomerates. This not only destroyed the structural continuity of the insulating substrate but also caused the sensing network to break. In contrast, the synchronous process of the present invention, through the synergistic effect of dispersion and cross-linking, allows the sensing material to diffuse in situ in the molten state of polyethylene and be anchored by the three-dimensional network structure formed by cross-linking. This achieves a deep compatibility of physical uniform dispersion and chemical stability, solving the technical problem that traditional processes cannot simultaneously achieve both dispersion and stability.

[0091] Comparative Example 2 skipped the pre-dispersion liquid preparation step and directly blended the undispersed sensing material with the insulating substrate. The agglomeration particle size of the sensing material was ≥200nm, and the early warning accuracy was only 81%. This is because the initial agglomerates formed by the sensing material during storage need to be broken by the pre-dispersion step. Otherwise, they will further aggregate during the blending process, resulting in uneven distribution of the sensing network and insufficient signal recognition. This proves that the pre-dispersion step is a prerequisite for macroscopically uniform material compatibility.

[0092] In Comparative Example 4, due to insufficient synergistic dispersant ratio, the dielectric strength dropped to 19.2 kV / mm, and the sensing response time was extended to 35 s. This is because insufficient dispersant prevents it from fully coating the surface of the sensing material, thus failing to effectively inhibit agglomeration and making it difficult to form a stable chemical bond with the insulating substrate. This results in weak interfacial bonding and obstructed sensing signal transmission, confirming the scientific nature of the dispersant ratio limitation in this invention.

[0093] The results show that the technical solution of the present invention does not rely on a specific combination of materials, and the material type can be flexibly adjusted according to the needs of the scenario, taking into account both performance and cost. At the same time, all raw materials are industrial mass-produced products, and the process steps do not require high-precision special equipment, which meets the requirements of industrial production and has significant commercial application prospects.

[0094] The technical scope of this invention is not limited to the content described above. Those skilled in the art can make various modifications and variations to the above embodiments without departing from the technical concept of this invention, and all such modifications and variations should fall within the protection scope of this invention.

Claims

1. A method for preparing a material-compatible intelligent early warning insulated busbar, characterized in that, Includes the following steps: S1: Prepare insulating substrate, sensing material, synergistic dispersant and functional additives according to the preset ratio, and complete the classification pretreatment; S2: Mix the sensing material with a synergistic dispersant to prepare a pre-dispersion; S3: The pre-dispersed liquid is blended with the pre-molten insulating substrate, and in-situ dispersion and cross-linking reaction of the insulating substrate are performed simultaneously to prepare a composite melt; S4: Using a gradient temperature-controlled coating process, the composite melt is coated onto the surface of the pretreated conductive substrate to form an insulating layer; S5: Electrodes and early warning modules are integrated on the surface of the insulation layer. After constant temperature curing and performance calibration, a material-compatible intelligent early warning insulation busbar is produced.

2. The method for preparing a material-compatible intelligent early warning insulated busbar according to claim 1, characterized in that: In S1, the sensing material includes temperature sensing material, insulation aging sensing material and mechanical damage sensing material.

3. The method for preparing a material-compatible intelligent early warning insulated busbar according to claim 2, characterized in that: The temperature sensing material is selected from one or more of carbon nanotubes, graphene, and ceramic-based negative temperature coefficient thermistors.

4. The method for preparing a material-compatible intelligent early warning insulated busbar according to claim 2, characterized in that: The insulating aging sensing material is selected from one or more of polyaniline, polypyrrole, and acetylene black.

5. The method for preparing a material-compatible intelligent early warning insulated busbar according to claim 2, characterized in that: The mechanical damage sensing material is selected from one or more of carbon fiber, stainless steel conductive fiber, and amine-modified graphene oxide.

6. The method for preparing a material-compatible intelligent early warning insulated busbar according to claim 1, characterized in that: In S2, the synergistic dispersant is selected from one or a combination of two of silane coupling agents KH-550 and KH-560, and the total mass of the synergistic dispersant is not less than 50% of the total mass of the sensing material.

7. The method for preparing a material-compatible intelligent early warning insulated busbar according to claim 1, characterized in that: In S3, when the in-situ dispersion and crosslinking reaction with the insulating substrate are performed simultaneously, a crosslinking agent needs to be added. The crosslinking agent is selected from dicumyl peroxide.

8. The method for preparing a material-compatible intelligent early warning insulated busbar according to claim 1, characterized in that: In S1, the insulating substrate is polyethylene, and the functional additive is composed of nano-silica and UV-resistant carbon black.

9. The method for preparing a material-compatible intelligent early warning insulated busbar according to claim 1, characterized in that: In S4, during the gradient temperature control coating process, the coating mold is divided into an inlet section, a middle section, and an outlet section along the direction of travel of the conductive substrate, with the temperatures of the three sections set to 155-160℃, 145-150℃, and 135-140℃, respectively.

10. A material-compatible intelligent early warning insulated busbar, prepared by the method of any one of claims 1-9 for preparing the insulated busbar.

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