Filter Design and Optimization of Electromechanical Actuation Systems Using Search and Surrogate Algorithms for More-Electric Aircraft Applications

Gao, Yuan; Yang, Tao; Bozhko, Serhiy; Wheeler, Patrick; Dragicevic, Tomislav

Abstract

In this paper, a dc filter design and optimization problem is studied for dc electrical power distribution systems onboard more-electric aircraft. Component sizing models are built to serve as the basis of the optimization whose objectives are mass and power loss of this filter. A categorization strategy of search and surrogate algorithms are proposed and used for the target multi-objective optimization problem (MOOP). A genetic algorithm is utilized as a search algorithm to identify potential best solutions based on a set of filter sizing functions (subject to constraints). Additionally, two machine learning (ML) algorithms are considered as surrogate algorithms to address the same optimization problem. In the ML training process, a constraint violation model is applied since there are various constraints in optimization and this kind of classification model is relatively difficult to train. A support vector machine is applied for the constraint violation model after which two artificial neural networks are trained as the final surrogate model for mapping design variables to filter performance. To address these issues, a novel category of search and surrogate algorithms are proposed. Both algorithms are explored to solve the filter MOOP and their optimization results are compared at the end.

Citation

Gao, Y., Yang, T., Bozhko, S., Wheeler, P., & Dragicevic, T. (2020). Filter Design and Optimization of Electromechanical Actuation Systems Using Search and Surrogate Algorithms for More-Electric Aircraft Applications. IEEE Transactions on Transportation Electrification, 6(4), 1434-1447. https://doi.org/10.1109/tte.2020.3019729

• Journal Article Type: Article
• Acceptance Date: Jul 7, 2020
• Online Publication Date: Aug 26, 2020
• Publication Date: 2020-12
• Deposit Date: Jul 17, 2020
• Publicly Available Date: Aug 26, 2020
• Journal: IEEE Transactions on Transportation Electrification
• Electronic ISSN: 2332-7782
• Publisher: Institute of Electrical and Electronics Engineers
• Peer Reviewed: Peer Reviewed
• Volume: 6
• Issue: 4
• Pages: 1434-1447
• DOI: https://doi.org/10.1109/tte.2020.3019729
• Keywords: filter design; optimization; genetic algorithm (GA); search algorithm; surrogate algorithm; artificial neural network (ANN); more-electric aircraft (MEA)
• Public URL: https://nottingham-repository.worktribe.com/output/4772537
• Publisher URL: https://ieeexplore.ieee.org/document/9178291
• Additional Information: © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.