Multi-Objective Optimization Design of a Notch Filter Based on Improved NSGA-II for Conducted Emissions

• Main Authors: Lu Zhang, Hongjuan Ge, Ying Ma, Jianliang Xue, Huang Li, Michael Pecht
• Format: Article
• Language: English
• Published: IEEE 2020-01-01
• Series: IEEE Access
• Subjects: Suppression conducted emission; multi-objective optimization; NSGA-II; BP neural network; notch filter; autotransformer rectifier
• Online Access: https://ieeexplore.ieee.org/document/9082631/
• ISSN: 2169-3536

This paper develops an improved non dominated sorting genetic algorithm II (NSGA-II) based on objective importance vector γ, abbreviated as γ-NSGA-II. Different importance levels for the multiple objectives are incorporated in the objective importance vector, which is applied to determine the individual selection of sorting individuals in the critical layer. And such an individual selection strategy is developed to the NSGA-II algorithm in order to obtain the optimized solution for a task which has multiple objectives with different importance. The differences between the γ-NSGA-II algorithm and the traditional NSGA-II algorithm are discussed in detail. A notch filter is designed for the conducted emission suppression of a transformer rectifier unit (TRU) used in C919 flight testing, and then the parameters optimization design of a notch filter is discussed and conducted based on the γ-NSGA-II algorithm. The non-linear relationship between the filter's parameters and the suppression effect of the conducted emission is also discussed with the help of an electromagnetic compatibility (EMC) evaluation model based on a back propagation (BP) neural network. The experimental results show that the optimized design of the notch filter is effective and the improved γ-NSGA-II algorithm be more specific.