Artificial Intelligence Aided Design for Film Capacitors

• Published in: 2022 IEEE International Conference on High Voltage Engineering and Applications (ICHVE) pp. 1 - 4
• Main Authors: Zhang, Yong-Xin, Chen, Fang-Yi, Feng, Qi-Kun, Liu, Di-Fan, Pei, Jia-Yao, Zhong, Shao-Long, Yang, Zhe, Dang, Zhi-Min
• Format: Conference Proceeding
• Language: English
• Published: IEEE 25.09.2022
• Subjects: artificial intelligence; artificial neural network; back propagation; Capacitors; design; Dielectric materials; film capacitor; Industries; Mass production; Predictive models; Production; Technological innovation
• ISSN: 2474-3852
• DOI: 10.1109/ICHVE53725.2022.9961843

Driven by energy-related demands and the efforts of many researchers, film capacitors, with the stability of their electrical values over long durations, have become key devices in many fields, especially high current pulse loads or high AC loads in electrical systems application scenarios. With the increase in application requirements and the expansion of the application range from commercial and military, the personalized customization of film capacitors that are different from the mass production for specific application conditions becomes more and more important. At the same time, the efficiency and greening of the production process are also looking forward to the innovation of the design system of film capacitors. However, the production process of film capacitors is complex, and it is difficult to derive the functional relationship between production parameters and product performance. On the other hand, the historical accumulation of the film capacitor industry makes the relevant data resources relatively abundant. Because of the above situation, based on the Back Propagation (BP) neural network theory, this paper builds a film capacitors design model by learning the design and performance data of 54,604 film capacitors, thereby establishing the relationship between material types and capacitances of capacitors. After that, according to the established model and the given dielectric materials, the capacitances of produced film capacitors are predicted, and then the appropriate dielectric materials are screened out through reverse design according to the established model and the expected capacitances. Furthermore, this paper analyzes the distribution characteristics of the predicted value and absolute error under the two prediction directions.