Insulation recovery method and system for power distribution network line nodes

Abstract

The invention discloses an insulation recovery method and system for a power distribution network line node, and the method comprises the following steps: constructing a self-adaptive sealing operation cavity at a to-be-processed line node, and changing the rigidity through triggering so as to achieve the tight fitting with the shape of the node; in the sealed operation cavity, the surface of the joint is subjected to in-situ cleaning and activating treatment; through a multi-channel injection system, injecting a curable insulating composite material into the sealed operation cavity in a regionalized and time-sequence-divided manner; in the injection process or after injection is completed, at least one energy field is applied to the insulation composite material so as to trigger rapid curing of the insulation composite material, and the functional filler in the insulation composite material forms an ordered arrangement structure; in the curing process and after curing is completed, the insulation state is monitored and diagnosed in real time through a sensor array integrated in the cavity till the preset recovery standard is met. According to the invention, high-efficiency, high-quality and self-adaptive insulation repair of the distribution network line nodes can be realized.

Description

Technical Field

[0001] This invention relates to the field of power equipment maintenance technology, specifically to an insulation restoration method and system for distribution network line nodes, which is particularly suitable for rapid and reliable insulation repair of 10kV and above voltage level distribution lines after power-off work or when local insulation damage occurs. Background Technology

[0002] Distribution network lines contain numerous joints, piercing clamps, switch terminals, and other nodes, which are weak points in insulation. After live-line work (such as bypass work or temporary power supply), or long-term operation affected by the environment, the insulation of these nodes may be damaged or require resealing. Traditional insulation restoration methods mainly include wrapping with insulating tape, installing heat-shrink tubing or cold-shrink tubing, and injecting insulating adhesive. However, existing insulation restoration methods have the following main shortcomings: 1) The quality of insulation restoration depends on the skills of maintenance workers. The tightness of manual winding and the layering method vary from person to person, resulting in poor consistency. The quality cannot be guaranteed, and air gaps or wrinkles are easily generated, leading to partial discharge. 2) Traditional methods, such as multi-layer tape wrapping or heat shrink tubing installation, usually take more than 1 hour; the curing time is even longer, for example, some potting adhesives require several hours, which prolongs the time of abnormal operation of the circuit. 3) Prefabricated heat shrink tubing and cold shrink tubing have limited specifications, making it difficult to perfectly fit complex nodes of different shapes and sizes (such as parts with sensors or irregularly shaped fittings), resulting in unsatisfactory sealing performance; 4) The filling status, interface bonding and curing degree of the insulation material cannot be monitored in real time during the operation. The final quality can only be verified by post-operation testing, which poses a potential risk. Summary of the Invention

[0003] To address the technical problems of long insulation restoration time, low efficiency, uncontrollable quality, and unsatisfactory sealing effect, this invention provides an insulation restoration method and system for distribution network line nodes, which can achieve efficient, high-quality, and adaptive insulation repair of distribution network line nodes, significantly improving the reliability of the line after the operation.

[0004] In a first aspect, the present invention provides an adaptive insulation restoration method for distribution network line nodes, comprising the following steps: S1: Construct an adaptive sealing working cavity at the line node to be processed. The cavity is at least partially made of a variable stiffness smart material and can change its stiffness by triggering to achieve a tight fit with the shape of the node. S2: In the sealed working cavity, the node surface is cleaned and activated in situ; S3: Through a multi-channel injection system, a curable insulating composite material is injected into the sealed working cavity in a regional and time-sequential manner; S4: During or after the injection process, at least one energy field is applied to the insulating composite material to trigger its rapid curing, and a directional control field is applied during the curing process to make the functional fillers in the insulating composite material form an ordered arrangement structure. S5: During and after the curing process, the insulation status is monitored and diagnosed in real time by a sensor array integrated in the cavity until the preset recovery standard is met.

[0005] Preferably, in step S1, constructing the adaptive sealing working cavity specifically includes: The working cavity, made of the aforementioned variable stiffness smart material and in a flexible state, is wrapped around the outside of the node; The variable stiffness smart material is triggered to change from a flexible state to a rigid state, thus fixing the shape of the cavity; The negative pressure adsorption unit located at the edge of the cavity is activated to enhance the sealing between the cavity and the node surface. Preferably, in step S3, the regional and time-sequential injection specifically includes: first, injecting a layer of highly adhesive interface adhesive; then injecting the main insulating composite material, and simultaneously or after injecting the main insulating composite material, applying a directional electric field to orient the sheet-like nanofillers dispersed in the main insulating composite material along the direction of the electric field.

[0006] Preferably, in step S4, applying at least one energy field is applying a combined energy field of ultraviolet light and microwaves; wherein, ultraviolet light is used to trigger rapid gelation of the material surface to form a closed shell, and microwaves are used to penetrate the closed shell to uniformly and deeply solidify the interior of the material.

[0007] Preferably, in step S4, the directional control field is one or more of an electric field, a magnetic field, or a shear flow field, used to control the functional filler to form a gradient insulation structure distributed along an equipotential surface.

[0008] Preferably, in step S5, the real-time monitoring and diagnosis includes: The sealing performance is determined by monitoring the humidity inside the cavity using a capacitive sensor. The dielectric constant and loss factor of the injected material are monitored online using dielectric sensors to control the curing process. When the changes in the dielectric constant and loss factor are monitored to tend towards the set stable state, the main curing stage is determined to be completed. Subsequently, a partial discharge diagnostic voltage is applied to evaluate the insulation strength of the restored node.

[0009] Preferably, before step S1, the method further includes the following steps: acquiring the three-dimensional topographic information of the line node to be processed, and generating a personalized sequence of operation parameters to guide steps S1 to S5 based on the information and a preset digital twin process model. In a second aspect, the present invention provides an adaptive insulation rapid recovery system for implementing the above method, comprising: A sealing and working chamber unit for performing steps S1 and S2, comprising a cavity made of the variable stiffness smart material; The material injection and field control unit, used to perform steps S3 and S4, includes a multi-component material delivery pump, a multi-channel injection head, and an energy field generator. A sensing and intelligent control unit, used to perform step S5, includes the sensor array, controller, and processor.

[0010] Preferably, the sensing and intelligent control unit is configured to: receive real-time data from the sensor array; compare the real-time data with the predicted state of a pre-stored digital twin process model; and dynamically adjust the operating parameters of the material injection and field control unit and the sealing and operation chamber unit based on the comparison results to achieve closed-loop adaptive control.

[0011] It also includes a mobile platform on which the sealing and working chamber unit, the material injection and field control unit, and the sensing and intelligent control unit are integrated; the mobile platform is a drone, a robotic arm, or a track robot, used to transport and position the system to a power-on or power-off line node.

[0012] Compared with the prior art, the present invention has at least the following beneficial effects: 1) By using energy field triggering technologies such as ultraviolet-microwave synergistic curing, the critical curing time is significantly shortened, and the entire insulation restoration process can be completed quickly within a specified time (e.g., no more than 1 hour). The restoration speed is fast and the efficiency is high, greatly reducing the abnormal operation time of the line.

[0013] 2) By utilizing variable stiffness smart materials and negative pressure adsorption technology, it can automatically adapt to complex nodes of different sizes and shapes, with stronger adaptability; it can form a working cavity with excellent airtightness, making the seal more reliable, thus laying the foundation for high-quality repair.

[0014] 3) By using directional control fields such as electric fields to arrange sheet-like nanofillers in an orderly manner, a biomimetic insulation structure was constructed, which significantly improved the breakdown strength, resistance to tracking, and resistance to partial discharge of the restored insulator.

[0015] 4) By integrating multi-sensor real-time monitoring and digital twin-based closed-loop control, adaptive optimization of process parameters is achieved, ensuring consistency in each operation. This results in a more intelligent and controllable process with more consistent and reliable quality. It eliminates the influence of human factors that rely on worker skills and verifies repair quality in real time through online diagnostics.

[0016] 5) It integrates cleaning, sealing, injection, curing and testing functions into one unit, and can be combined with a robot platform to realize automated and unmanned operation in remote or high-risk scenarios, thereby improving safety and operating range.

[0017] 6) It realizes closed-loop adaptive control based on digital twin and real-time sensor feedback, which can dynamically optimize process parameters, ensure that the quality of operation is not affected by human and environmental factors, and has the ability to continuously learn and optimize. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is a flowchart illustrating the steps of an insulation restoration method for a distribution network line node, as provided in an embodiment of the present invention.

[0020] Figure 2 This is a schematic flowchart of an insulation restoration method for distribution network line nodes provided in an embodiment of the present invention.

[0021] Figure 3 This is a system framework diagram of an insulation restoration system for distribution network line nodes provided in an embodiment of the present invention.

[0022] Figure 4 A schematic diagram illustrating the evolution of the microstructure of a mid-field oriented insulating composite material provided in an embodiment of the present invention.

[0023] Figure 5 The diagram illustrates the adaptive closed-loop control principle based on digital twin and multi-sensor feedback, as provided in this embodiment of the invention. Detailed Implementation

[0024] The embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0025] It should be understood that the described embodiments are merely some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0026] refer to Figure 1 This invention provides an insulation restoration method for distribution network line nodes, comprising the following steps: S1: Construct an adaptive sealing working cavity at the node of the line to be processed. The cavity is at least partially made of a variable stiffness smart material and can change its stiffness by triggering to achieve a tight fit with the shape of the node. Among them, the line nodes to be processed, as work nodes, refer to physical connection points or access points formed in overhead or cable lines of the distribution network due to structural connections, electrical connections, or functional intervention requirements. These nodes are interruptions in the continuity of the insulation layer of the line conductors (wires), and are essentially inherently weak links in the insulation. The line nodes to be processed specifically include, but are not limited to, the following locations: Wire connectors: such as the wire connection points at straight-line splicing conduits and tension clamps; Branch connection point: such as a branch connection point achieved through a piercing clamp; Equipment connection terminals: such as the incoming and outgoing wiring terminals for equipment like pole-mounted switches, disconnect switches, drop-out fuses, and surge arresters; Work intervention point: Temporary joints or equipment connection points for installation and removal during uninterrupted power supply work (such as bypass work or temporary power supply).

[0027] S2: In the sealed working chamber, the node surface is cleaned and activated in situ; S3: Through a multi-channel injection system, curable insulating composite material is injected into the sealed working cavity in sections and at different times; S4: During or after injection, at least one energy field is applied to the insulating composite material to trigger its rapid curing, and a directional control field is applied during the curing process to make the functional fillers in the insulating composite material form an ordered arrangement structure. S5: During and after the curing process, the insulation status is monitored and diagnosed in real time by a sensor array integrated into the cavity until the preset recovery standard is met.

[0028] Step S5, which involves real-time monitoring and diagnosis of the insulation status, refers to real-time monitoring and diagnosis of key parameters, including the sealing environment, curing process, and final insulation performance, until the formed insulation recovery layer meets the preset recovery standards. Example 1

[0029] In an embodiment of the present invention, reference is made to... Figure 2 Taking the uninterrupted insulation repair of a puncture clamp joint on an operating 10kV line as an example.

[0030] Prior to step S1, there are preparation and positioning steps. The operator or robot moves the system to the vicinity of the work node, aligning the sealing and work chamber unit with the puncture clamp to be treated.

[0031] The core of step S1 is to construct an adaptive sealed working cavity at the line node to be processed. Step S1 aims to create a well-sealed working space isolated from the outside world for subsequent operations, and it further includes the following sub-steps: Step S1.1: The operator places a flexible, deformable cavity over the puncture clamp and the two side leads to be treated. The cavity consists of an outer silicone skin and an inner shape memory polymer middle layer, and is initially soft.

[0032] Step S1.2: By passing a low-voltage current through the shape memory polymer layer, it is transformed from a flexible state to a rigid state within 30 seconds. The cavity then takes shape and fits tightly against the irregularly shaped clamp and wire surface.

[0033] Step S1.3: Activate the miniature negative pressure pumps arranged in a ring around the edge of the cavity to create a stable -50kPa negative pressure at the interface between the cavity and the wires and fittings. This effectively adsorbs and compensates for microscopic unevenness, ensuring a complete seal of the cavity. Confirm that the internal ambient humidity has dropped below 10%RH using the built-in humidity sensor, indicating a successful seal.

[0034] The key point of step S2 is to perform in-situ cleaning and activation treatment on the surface of the circuit node to be treated within the sealed working chamber. Step S2 aims to improve the interfacial adhesion between the node surface and the subsequently injected material, and it further includes the following sub-steps: Step S2.1: Introduce low-temperature atmospheric pressure plasma into the sealed cavity for 60 seconds. The plasma flow effectively removes organic contaminants, moisture, and weak oxide layers from the wire insulation layer and the surface of the metal clamp.

[0035] Step S2.2: Simultaneously, the active particles in the plasma chemically activate the silicone rubber insulating surface and the metal surface, generating more active groups (such as hydroxyl groups) that are easy to chemically bond on their surfaces.

[0036] Step S3 involves injecting curable insulating composite material into the sealing cavity in a zoned and sequential manner using a multi-channel injection system. Step S3 aims to precisely fill and optimize the insulation structure, and further includes the following sub-steps: Step S3.1: First, inject approximately 10 ml of silane coupling agent interface treatment agent through the dedicated injection channel A located at the bottom of the sealing chamber. This low-viscosity liquid preferentially spreads and wets the metal-insulation interface at the bottom of the chamber, forming a transition layer that enhances adhesion after standing for 30 seconds.

[0037] Step S3.2 (Injection of main insulating material and application of control field): Subsequently, approximately 150 ml of insulating composite material is simultaneously injected through two symmetrical injection channels B and C located in the upper part of the sealed working chamber. This material is a two-component addition-type liquid silicone rubber containing boron hydride nanosheets. Simultaneously with the injection initiation, a flat plate electrode surrounding the chamber is activated, applying a DC electric field with a strength of 2.5 kV / mm. Under the influence of the electric field, the sheet-like nanofillers in the material begin to align oriented along the direction of the electric field (i.e., approximately perpendicular to the surface of the conductor) driven by both the injection flow and the electric field force.

[0038] The core of step S4 is applying an energy field to trigger rapid solidification and regulate the filler arrangement. Step S4 aims to achieve a rapid and controllable transformation of the material from a liquid to a solid state and to fix the optimized microstructure. It further includes the following sub-steps: Step S4.1: After injection, immediately activate the UV-LED array (wavelength 385nm, intensity 400 mW / cm²) arranged around the periphery of the cavity and irradiate for 90 seconds. The ultraviolet light penetrates the outer shell of the cavity, triggering a rapid photopolymerization reaction on the surface layer of the material (approximately 1-2mm deep), forming a robust gel "shell" that locks in the overall shape and prevents the material from deforming due to gravity or internal pressure.

[0039] Step S4.2 (Microwave Deep Curing and Orientation Completion): Next, the microwave generator (2.45 GHz, 350W) is activated to irradiate the cavity for 180 seconds. Microwaves penetrate the cured outer shell, uniformly and efficiently heating the internal material, initiating an addition reaction in the silicone rubber to achieve complete curing. The previously applied directional electric field continues to act during microwave curing, ensuring that the nanosheets complete their final orientation and fixation before the material viscosity rapidly increases to solidification, forming a shape like... Figure 4 The "brick-mud" ordered structure is shown.

[0040] Step S5 involves real-time monitoring and online diagnostics. Step S5 runs through steps S3 and S4 and ultimately confirms the recovery quality. It further includes the following sub-steps: Step S5.1: During steps S3 and S4, the change curve of the material's complex dielectric constant is monitored in real time by a thin-film dielectric sensor integrated into the inner wall of the cavity. The main controller compares this curve with a pre-stored ideal curing model. When a lag in the curing reaction rate is detected, the microwave power is automatically fine-tuned to 380W to achieve closed-loop adaptive control of the curing kinetics.

[0041] Step S5.2: After S4 ends and the material is fully cured (approximately 9 minutes in total from the start of injection), the system automatically executes a diagnostic procedure: a 12kV (approximately 1.7 times the peak phase voltage) power frequency withstand voltage is applied for 1 minute through the same pair of flat plate electrodes. Simultaneously, an ultra-high frequency sensor monitors the partial discharge signal. The results show that the partial discharge level stabilizes below 3pC, far below the ≤10pC requirement in the DL / T1309-2013 standard.

[0042] Step S5.3: After successful diagnosis, the system cuts off the heating current to the shape memory polymer, allowing it to cool naturally and regain its flexibility, while simultaneously releasing the negative pressure. The operator can then easily remove the working chamber, revealing a new insulator with a regular shape and excellent performance.

[0043] Using this method, the total operation time from the start of cavity construction to completion of diagnosis and readiness for power supply is significantly shortened and made more controllable. For example, the entire process time can be controlled within 30 minutes (compared to at least 3 hours for traditional methods). The critical process from material injection to complete curing takes no more than 15 minutes (compared to at least 2 hours for traditional methods from injection and settling to curing). The restored joint exhibits significantly better power frequency withstand voltage and partial discharge performance than newly installed traditional joints of the same type. Moreover, the operation process is minimally affected by human factors, resulting in stable and reliable quality (compared to the effect of traditional manual insulation wrapping, in withstand voltage tests, approximately 30% of the samples showed partial discharge in the range of 10-15 pC, barely passing the test, and accompanied by a faint audible discharge sound, indicating the presence of unavoidable air gaps or delamination inside the wrapping). Example 2

[0044] In an embodiment of the present invention, the target for repair is a section of 10kV conductor whose insulation layer has irregular scratches approximately 5cm long and 2mm deep due to external force. This demonstrates the adaptability of the insulation restoration method for distribution network line nodes of the present invention to the repair of non-standard nodes (or irregularly shaped damaged points). The following only describes the features that differ from Embodiment 1. For the same features or parts, please refer to the relevant description in Embodiment 1.

[0045] In step S1, the flexible cavity perfectly fits the cylindrical surface of the wire and the damaged recesses, achieving a seal on the irregular three-dimensional curved surface through rigidification and negative pressure adsorption.

[0046] In step S3, by adjusting the path and speed of the injection needle, it is ensured that the insulating composite material can completely fill the depressed wound and smoothly transition with the surrounding healthy insulation layer.

[0047] In step S4, the applied directional electric field is perpendicular to the surface of the conductor, causing the nanosheets to form an enhanced barrier perpendicular to the surface of the conductor in the damaged repair area, effectively blocking axial creepage.

[0048] The final repaired area had a smooth appearance and met electrical performance standards, demonstrating the adaptability and repair effectiveness of the method of the present invention for complex geometries. Example 3

[0049] In an embodiment of the insulation restoration system for distribution network line nodes of the present invention, reference is made to... Figure 3 The insulation restoration system for distribution network line nodes mainly consists of three major units: a sealing and operation chamber unit, a material injection and field control unit, and a sensing and intelligent control unit. These three units are integrated into a mobile box or robot platform.

[0050] The core of the sealing and working chamber unit is an adaptive sealing working cavity. This sealing working cavity adopts a three-layer structure: the outer layer is a flexible, high-strength silicone skin; the middle layer is a polyurethane-based smart gel doped with shape memory alloy wires, which can be heated to above its transition temperature (e.g., 65°C) by passing a low current (e.g., <5A), thus transforming it from a soft state to a hard state; the inner layer is a sprayed semiconductor silicone grease coating used to uniformly apply the electric field. The cavity edge has micro-negative pressure adsorption holes distributed in a ring, connected to a vacuum pump.

[0051] The material injection and field control unit includes a two-component material reservoir (component A: addition-cured liquid silicone rubber; component B: a mixture of boron hydride nanosheets, UV photoinitiator, and microwave absorber), a precision metering pump and static mixer, and a multi-channel injection head with three independent needles. The unit also integrates a UV-LED array light source (wavelength 365nm) and a microwave generator (frequency 2.45GHz, power adjustable 0-800W). A pair of planar electrodes is arranged around the cavity to generate a uniform directional electric field (adjustable 0-5kV / mm).

[0052] The sensing and intelligent control unit integrates its controller (such as a PLC or embedded industrial computer) within the enclosure. The inner wall of the enclosure houses a miniature humidity sensor, a fiber Bragg grating temperature / strain sensor, and a pair of thin-film dielectric sensors. All sensors are connected to the controller. The controller contains pre-stored digital twin process models of typical nodes and can communicate with the back-end monitoring center via a wireless module.

[0053] When performing insulation restoration on a 10kV cable puncture clamp joint, the operator or robot first moves the system near the work node, aligning the sealing and work chamber unit with the clamp to be treated. Next, the flexible, adaptive sealing work chamber encases the clamp and the conductors on both sides. Then, a 3A current is passed through the middle layer of smart gel for 30 seconds, raising its temperature to 70°C, making the material rigid and allowing the chamber to perfectly conform to the complex contours of the node. The vacuum pump is then activated, creating a negative pressure suction port of -60kPa to ensure a tight seal between the chamber and the conductors and fitting surfaces. Afterward, the humidity sensor reading stabilizes below 5%RH (Relative Humidity), confirming a good seal.

[0054] Low-pressure (e.g., 100W) air plasma is then introduced into the cavity for 60 seconds to effectively remove surface contaminants and moisture, and activate the silicone rubber and metal surfaces, improving the adhesion of subsequent materials. The controller starts the metering pump, first injecting approximately 10ml of a special interface agent (mainly a viscous silane coupling agent solution) into the bottom of the cavity through an injection needle, allowing it to stand for 30 seconds to spread. Then, the main injection program is started. Components A and B are mixed in a 1:1 volume ratio using a static mixer, and then the liquid composite insulating material is symmetrically injected from both sides of the cavity through two other needles. The injection rate is adjusted in real time according to the cavity volume, with a total injection volume of approximately 150ml. When 80% of the injection is complete, the flat electrode is activated, applying a 3kV / mm DC electric field for 3 minutes. Under the action of this electric field, boron hydride nanosheets (e.g., boron nanosheets suspended in the liquid silicone rubber)... Figure 4 (As shown) They rapidly rotate along the direction of the electric field and arrange themselves in an orderly manner.

[0055] Immediately after injection, the UV-LED array light source is activated to irradiate the cavity surface at an intensity of 500mW / cm² for 60 seconds. The UV light triggers rapid gelation of the material surface layer (approximately 1-2mm deep), forming a closed shell that prevents material from flowing and locks in its shape.

[0056] Next, the microwave generator was activated, emitting microwaves at a power of 400W for 180 seconds. The microwaves penetrated the cured outer shell, uniformly and efficiently heating the internal materials, triggering the addition reaction of the silicone rubber to fully cure. Fiber optic sensor monitoring showed that the internal temperature of the material rose to 120°C within 120 seconds and remained stable before decreasing, indicating the completion of the reaction.

[0057] Throughout the injection and curing process, the thin-film dielectric sensor 323 continuously monitors the dielectric constant (ε) and loss factor (tanδ) of the material. The controller compares the real-time ε-t curve with the ideal curing curve in the digital twin model and fine-tunes the microwave power to ensure optimal curing quality.

[0058] After curing is complete (approximately 8 minutes from start to finish), the system automatically executes a diagnostic procedure: applying an 8kV (approximately 1.5 times the peak phase voltage) power frequency withstand voltage through the electrodes for 1 minute, while simultaneously monitoring the partial discharge signal. In this embodiment, the measured partial discharge quantity is <5pC, far below the standard requirement of 10pC.

[0059] After successful diagnosis, the controller cuts off the heating current to the smart gel, allowing it to cool naturally and regain its flexibility, while simultaneously releasing the negative pressure. The operator can then easily remove the restored insulation node. The entire process (steps S1-S5) takes approximately 25 minutes.

[0060] In contrast, if traditional insulating tape is used to manually wrap the same specification piercing clamp, a skilled worker would use a three-layer overlap of semi-conductive tape, self-adhesive insulating tape, and waterproof tape, which would take approximately 45 minutes. Afterward, it must be left to stand for at least 2 hours to allow stress relaxation before the withstand voltage test can be conducted. The test results showed that the partial discharge fluctuated between 10-20 pC, and a slight air gap discharge sound was heard. Example 4

[0061] This embodiment is described in detail. Figure 5 How is the closed-loop control principle shown implemented in the insulation recovery process?

[0062] The closed-loop control principle aims to ensure that every insulation restoration operation meets the preset performance standards (partial discharge <5pC, complete curing, and no interface defects).

[0063] Step 1': Digital Twin Model Initialization Before the operation begins (before step S1), the system calls the digital twin process model. This digital twin process model generates a predicted ideal process curve based on the following input parameters, including node geometry parameters (clamp dimensions and conductor diameter obtained through laser scanning or pre-stored drawings), material parameters (batch number of the A / B component insulation composite material used that day, as well as its viscosity and reactivity data), ambient temperature (real-time ambient temperature and humidity), and target performance (preset final performance indicators, such as tanδ<0.005).

[0064] Step 2': Real-time data acquisition and comparison (closed-loop feedback) During steps S3 (injection) and S4 (curing), the system performs the following real-time closed-loop control: including dielectric property monitoring and curing control in step 2.1' and temperature field uniformity control in step 2.2'. The dielectric property monitoring and curing control in step 2.1' acquires real-time data (e.g., a thin-film dielectric sensor measures the material's relative permittivity and loss factor tanδ every 10 seconds), model comparison (the main controller compares the measured tanδ-t curve with the ideal curing curve predicted by the digital twin model in real time), and control decisions and execution. The control decisions and execution involve the following three scenarios: Scenario A: If the measured tanδ rise rate is slower than the predicted curve, it indicates that the curing reaction rate is too slow. The controller then sends a command to the microwave generator to increase the power from 350W to 380W to accelerate the reaction.

[0065] Scenario B: If the measured tanδ peak appears too early and drops rapidly, it indicates that the reaction may be too fast, posing a risk of internal stress. The controller should then appropriately reduce the microwave power (e.g., to 330W) and extend the microwave action time by 30 seconds.

[0066] Scenario C: If the tanδ curve closely matches the prediction, then maintain the current parameters.

[0067] Step 2.2', temperature field uniformity control, includes real-time data monitoring and control decisions based on the monitoring results. For example, distributed fiber optic temperature sensors monitor the temperature at different locations within the cavity. If a temperature difference exceeds 15°C, the controller adjusts the field distribution mode of the microwave generator or briefly pauses the microwave operation and activates a small circulating fan within the cavity to ensure uniform heat distribution and prevent localized overheating or incomplete curing.

[0068] Step 3': Adaptive Judgment of Final Diagnosis In the final diagnostic stage of step S5, the closed-loop control still functions. This is mainly reflected in the adaptive application of the diagnostic voltage and the decision-making process for handling non-conforming products.

[0069] Regarding the adaptive application of the diagnostic voltage, the system does not mechanically apply a 12kV voltage. Instead, it fine-tunes the diagnostic voltage based on the final stable tanδ value during the curing process. If the final tanδ is extremely low (e.g., 0.002), indicating excellent material properties, the system may apply a standard 12kV.

[0070] If the final tanδ approaches the upper limit (e.g., 0.0045), the system adopts a more conservative strategy: first apply a lower voltage (e.g., 8kV) to test the waters, and after confirming that there are no abnormalities, gradually increase it to 12kV.

[0071] Regarding the handling of non-conforming products, if a non-conformance is diagnosed (PD > 5pC), the control model will analyze the cause. For example, if it is determined that the problem is due to poor local interface bonding, the system may automatically initiate a secondary repair procedure: re-execute steps S2 (activation) and S3 (local supplementary injection), instead of simply reporting an error.

[0072] Step 4': Model Self-Learning and Optimization After each operation is completed, the system uploads the complete data package of this operation (environmental parameters, actual process parameters, sensor data throughout the process, and final performance) to the backend cloud.

[0073] Digital twin models utilize this new data to train themselves and optimize parameters through machine learning algorithms.

[0074] For example, after multiple trials, it was found that when the ambient temperature was below 10°C, an initial microwave power preset of 370W yielded a better curing curve than 350W. The model will then automatically update, and in future trials under low-temperature conditions, it will automatically adopt the new optimized parameters.

[0075] Through the above closed-loop adaptive control, the present invention achieves: 1) Regardless of who the operator is or how the environment changes, the system can automatically adjust the process to the optimal path to ensure stable and reliable output results.

[0076] 2) Real-time intervention avoids process defects such as incomplete material curing, overheating degradation, and excessive internal stress.

[0077] 3) The system has self-learning capabilities, and its process control will become more and more precise and efficient as the number of times it is used increases.

[0078] Preferably, before step S1, the method further includes the following steps: acquiring the three-dimensional topographic information of the line node to be processed, and generating a personalized sequence of operating parameters to guide steps S1 to S5 based on the information and a preset digital twin process model. The guiding role of the step of acquiring the three-dimensional topographic information of the line node to be processed and generating a personalized sequence of operating parameters to guide steps S1 to S5 based on the three-dimensional topographic information and a preset digital twin process model is reflected in the following aspects: 1) Regarding the acquisition of 3D topographic information, precise 3D dimensions, surface curvature, and irregular features (such as protrusions, depressions, and seams) of nodes can be obtained through methods such as laser scanning, structured light scanning, and 3D cameras mounted on UAVs. This data forms a 3D model of the node, which serves as input for subsequent process planning and simulation optimization. 2) Regarding the role of the digital twin process model, the digital twin process model is a virtual simulation system that includes: a material database (recording the curing kinetics, flow characteristics, electric field / temperature response, etc. of insulating composite materials under different conditions), a process knowledge base (optimized historical data of process parameters such as curing time, electric field strength, and temperature field distribution), a node structure library (geometric features and typical failure modes of common node types such as puncture clamps, joints, and switch terminals), and a simulation engine (capable of simulating the entire process from cleaning and injection to curing and predicting the final insulation performance). 3) Regarding the generation and guidance of personalized operation parameter sequences, the digital twin process model automatically generates a set of operation parameter sequences for a specific node based on the input 3D topographic information, which guides the execution of steps S1 to S5. In guiding step S1, during the construction of the adaptive sealing operation cavity, the initial shape and rigidity trigger parameters of the cavity are recommended based on the node's shape and size; for example, for irregular joints, it is suggested which areas of the cavity should have increased flexibility and which areas should be pre-formed to ensure fit. In guiding step S2, during cleaning and activation treatment, the power, duration, and gas type of plasma treatment are recommended based on the surface material (silicone rubber, metal, ceramic) and degree of contamination; for example, argon plasma is recommended for metal surfaces, and air plasma is recommended for silicone rubber surfaces. In guiding step S3, regarding the path and timing of material injection, the movement path, injection sequence, and flow rate of the multi-channel injection head are planned based on the node's geometric characteristics; for example, for areas with depressions, interface agents are injected first; for complex structures, a strategy of injection from the inside out, in layers, is adopted. When applying the energy field and control field in guidance step S4, the UV intensity, microwave power, and electric / magnetic field strength and direction are recommended based on the node structure and material thickness. For example, for thick-walled repair, it is recommended to extend the microwave time and adjust the electric field direction to optimize the filler arrangement. In guidance step S5, regarding the threshold setting for monitoring and diagnosis, the dielectric constant variation curve, partial discharge threshold, and diagnostic voltage value are set according to the node type and target performance. For example, for critical nodes (such as switch terminals), stricter partial discharge limits (such as ≤3pC) are set. 4) In terms of closed-loop feedback and dynamic adjustment during actual operation, the sensing and intelligent control unit continuously collects sensor data (such as temperature, dielectric constant, humidity, etc.) and compares it with the prediction curve in the digital twin model. If a deviation occurs (such as a slow curing rate), the system will adjust the process parameters in real time (such as increasing microwave power) to achieve adaptive closed-loop control (as described in Example 4 above).

[0079] Take the repair of a 10kV puncture clamp joint as an example.

[0080] First, a three-dimensional morphology was obtained by scanning, revealing that the diameters of the wires on both sides of the clamp were inconsistent and that there were slight pits on the surface.

[0081] Secondly, the digital twin model generates a parameter sequence. This guides steps S1: pre-stretching the cavity on the thin side of the conductor and pre-compressing the thick side; step S3: injecting the interface agent into the recesses first, then symmetrically injecting the main material; step S4: setting the electric field direction perpendicular to the conductor surface and extending the UV irradiation time to 100 seconds; and step S5: setting the tanδ stability threshold to, for example, 0.004, and the partial discharge diagnostic voltage to 12kV. Additionally, during execution and adjustment, if a slow curing rate is detected during operation, the system automatically adjusts the microwave power from 350W to 380W. Example 5

[0082] Furthermore, this embodiment also provides a B-component formulation of the insulating composite material used in the above-described method and system of the present invention. The B-component formulation of the insulating composite material comprises, by weight: 100 parts addition-type liquid silicone rubber base, 15 parts surface-modified boron hydride nanosheets (5-15 μm in diameter, 50-100 nm in thickness), 1.5 parts UV photoinitiator (e.g., benzophenone), 3 parts microwave absorber (hydroxylated iron oxide nanoparticles, approximately 20 nm in diameter), 5 parts self-healing microcapsules (urea-formaldehyde resin shell encapsulated with dimethyl silicone oil), and 0.1-2 parts platinum catalyst (added as a platinum catalyst solution with a commonly used concentration of 3000-5000 ppm, corresponding to a platinum metal content of approximately 10-50 ppm).

[0083] The above-mentioned insulating composite material B has a moderate viscosity (approximately 5000 cP) before curing, making it easy to inject. Under an electric field, the boron hydride nanosheets can be effectively oriented. Rapid and deep curing can be achieved under UV and microwave conditions. The contained self-healing microcapsules can rupture during later operation of the insulator due to micro-cracks, releasing a repair agent to achieve self-healing.

[0084] It should be noted that in this paper, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply these relationships. There is no such actual relationship or order between entities or operations. Furthermore, the terms "including" and "package" do not apply. The word "comprise" or any other variation thereof is intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprises a..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0085] In this embodiment of the invention, the term "and / or" describes the relationship between associated objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. The character " / " generally indicates that the preceding and following associated objects have an "or" relationship.

[0086] The various embodiments in this specification are described in a related manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

[0087] In particular, the device embodiments are basically similar to the method embodiments, so they are described in a simpler way. For relevant details, please refer to the description of the method embodiments.

[0088] For ease of description, the above apparatus is described by dividing it into functional units / modules. Of course, in implementing this invention, the functions of each unit / module can be implemented in one or more software and / or hardware.

[0089] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. The storage medium can be a magnetic disk, optical disk, read-only memory (ROM), or random access memory (RAM), etc.

[0090] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for insulation restoration of distribution network line nodes, characterized in that, Includes the following steps: S1: Construct an adaptive sealing working cavity at the line node to be processed. The cavity is at least partially made of a variable stiffness smart material and can change its stiffness by triggering to achieve a tight fit with the shape of the node. S2: In the sealed working cavity, the node surface is cleaned and activated in situ; S3: Through a multi-channel injection system, a curable insulating composite material is injected into the sealed working cavity in a regional and time-sequential manner; S4: During or after the injection process, at least one energy field is applied to the insulating composite material to trigger its rapid curing, and a directional control field is applied during the curing process to make the functional fillers in the insulating composite material form an ordered arrangement structure. S5: During and after the curing process, the insulation status is monitored and diagnosed in real time by a sensor array integrated into the cavity until the preset recovery standard is met.

2. The insulation restoration method for distribution network line nodes according to claim 1, characterized in that, In step S1, constructing the adaptive sealing working cavity specifically includes: The working cavity, made of the aforementioned variable stiffness smart material and in a flexible state, is wrapped around the outside of the node; The variable stiffness smart material is triggered to change from a flexible state to a rigid state, thus fixing the shape of the cavity; The negative pressure adsorption unit located at the edge of the cavity is activated to enhance the sealing between the cavity and the node surface.

3. The insulation restoration method for distribution network line nodes according to claim 1 or 2, characterized in that, In step S3, the regional and temporal injection specifically includes: First, inject a layer of highly adhesive interface binder; Subsequently, the main insulating composite material is injected. At the same time or after the injection of the main insulating composite material, a directional electric field is applied to orient the sheet-like nanofillers dispersed in the main insulating composite material along the direction of the electric field.

4. The insulation restoration method for distribution network line nodes according to claim 1, characterized in that, In step S4, applying at least one energy field is applying a combined energy field of ultraviolet light and microwaves; wherein, ultraviolet light is used to trigger rapid gelation of the material surface to form a closed shell, and microwaves are used to penetrate the closed shell to uniformly and deeply solidify the interior of the material.

5. The insulation restoration method for distribution network line nodes according to claim 1, characterized in that, In step S4, the directional control field is one or more of an electric field, a magnetic field, or a shear flow field, used to control the functional filler to form a gradient insulation structure distributed along the equipotential surface.

6. The insulation restoration method for distribution network line nodes according to claim 1, characterized in that, In step S5, the real-time monitoring and diagnosis includes: The sealing performance is determined by monitoring the humidity inside the cavity using a capacitive sensor. The dielectric constant and loss factor of the injected material are monitored online using dielectric sensors to control the curing process. When the changes in the dielectric constant and loss factor are monitored to tend towards the set stable state, the main curing stage is determined to be completed. Subsequently, a partial discharge diagnostic voltage is applied to evaluate the insulation strength of the restored node.

7. The insulation restoration method for distribution network line nodes according to claim 1, characterized in that, Before step S1, the following steps are also included: obtaining the three-dimensional topographic information of the line node to be processed, and generating a personalized sequence of operation parameters to guide steps S1 to S5 based on the information and the preset digital twin process model.

8. An insulation restoration system for distribution network line nodes, used to implement the insulation restoration method for distribution network line nodes as described in any one of claims 1-7, characterized in that, include: A sealing and working chamber unit for performing steps S1 and S2, comprising a cavity made of the variable stiffness smart material; The material injection and field control unit, used to perform steps S3 and S4, includes a multi-component material delivery pump, a multi-channel injection head, and an energy field generator. A sensing and intelligent control unit, used to perform step S5, includes the sensor array, a controller, and a processor storing a digital twin process model.

9. The insulation restoration system for distribution network line nodes according to claim 8, characterized in that, The sensing and intelligent control unit is configured as follows: Receive real-time data from the sensor array; The real-time data is compared with the predicted state of the digital twin process model; Based on the comparison results, the operating parameters of the material injection and field control unit and the sealing and operation chamber unit are dynamically adjusted to achieve closed-loop adaptive control.

10. The insulation restoration system for distribution network line nodes according to claim 8, characterized in that, It also includes a mobile platform, on which the sealing and operation chamber unit, the material injection and field control unit, and the sensing and intelligent control unit are integrated; the mobile platform is a drone, a robotic arm, or a track robot, used to transport and position the system to the node of the line to be processed.

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