Tsinghua University successfully developed self-repairing insulation materials

On December 31, 2018, Prof. He Jinliang and Associate Professor Li Qi from the Department of Electrical Engineering of Tsinghua University published an online issue entitled “Using Superparamagnetic Nanoparticles” in the journal Nature Nanotechnology (Influence Factor 37.49). Research paper on Self-healing of electrical damage in polymers using superparamagnetic nanoparticles. This paper proposes a method for self-repair of electrical damage in solid insulating materials. For the first time, the self-healing of the electrical tree channel and the self-recovery of the insulating properties of the insulating material after electrical tree damage are achieved, while maintaining the basic electrical properties of the material. Affected. This self-healing strategy is widely applied to thermoplastic polymer insulation materials such as polyolefins, providing a new method for greatly improving the service life and reliability of power equipment and electronic equipment such as power cables.

In recent years, with the rapid development of the global energy Internet and UHV transmission technology, the voltage level of the power grid has gradually increased, and the scale of the power grid has been expanding. In order to maintain stable operation of the transmission network under increasing load conditions, it is necessary to continuously improve the reliability and service life of electrical equipment under extreme working conditions. The operating life of electrical equipment, especially high-voltage power equipment, often depends on the service life of the insulation components. The electrical tree defects formed by the insulation medium during long-term operation are the main causes of insulation damage. For a long time, the electrical tree defects of solid insulating materials are considered to be irreversible permanent damage. The research on electric tree aging is mainly to delay the development of electric trees by adding voltage stabilizers and electric tree blockers. However, the electric tree aging of the insulating material is difficult to avoid, and once the electric tree defect is formed, the insulation life is greatly reduced, and even the permanent damage of the device is generated.

In order to obtain an insulating material with both electrical damage repair function and high dielectric strength, the research team used polyolefin cable insulation as the substrate to utilize the entropy dissipation migration behavior of nanoparticles in the polymer, combined with superparamagnetic nanoparticles. The magnetocaloric effect achieves the targeted repeated repair of the electrical tree damage of the thermoplastic insulating material. The migration and diffusion behavior of nanoparticles during the repair process of electric tree damage were verified by molecular dynamics simulation and microscopic experimental characterization based on Gaussian chain model. Leakage current and partial discharge tests have shown that this self-repairing method can fully restore the electrical insulation properties of polyolefin insulation materials that generate electrical tree damage and maintain the same level of insulation as pure polyolefins in multiple repairs. The defect repair mechanism can be achieved by using a very low superparamagnetic nanoparticle loading (0.1 vol.% or less), thereby maintaining the electrical breakdown strength of the self-healing insulating material above 94% of the substrate (eg 490 kV). /mm), to meet the application needs of power energy fields such as ultra-high voltage cable transmission. In addition, for electrical equipment such as power electronics and electric vehicle wireless charging devices, this method is also expected to achieve live self-healing and online maintenance of insulation material damage in these fields.

The first author of the paper is Yang Yang, a 2014 Ph.D. student in the Department of Electrical Engineering of Tsinghua University. The authors are Professor He Jinliang from the Department of Electrical Engineering, Associate Professor Li Qi, and Professor Wang Qing from Pennsylvania State University. Collaborators include Dr. Gao Lei from the Department of Electrical Engineering, Associate Professor Hu Jun, Professor Zeng Yi, Associate Professor Qin Jian from Stanford University, and Professor Wang Shanxiang. The research was funded by the National Key Basic Research and Development Program 2014CB239500, and Professor He Jinliang was the project's chief scientist.