Surrogate role of machine learning in motor-drive optimization for more-electric aircraft applications

Motor drives form an essential part of the electric compressors,pumps,braking and actuation systems in the More-Electric Aircraft(MEA).In this paper,the application of Machine Learning(ML)in motor-drive design and optimization process is investigated.The general idea of using ML is to train surrogate models for the optimization.This training process is based on sample data collected from detailed simulation or experiment of motor drives.However,the Surrogate Role(SR)of ML may vary for different applications.This paper first introduces the principles of ML and then proposes two SRs(direct mapping approach and correction approach)of the ML in a motor-drive optimization process.Two different cases are given for the method comparison and validation of ML SRs.The first case is using the sample data from experiments to train the ML surrogate models.For the second case,the joint-simulation data is utilized for a multi-objective motor-drive optimization problem.It is found that both surrogate roles of ML can provide a good mapping model for the cases and in the second case,three feasible design schemes of ML are proposed and validated for the two SRs.Regarding the time consumption in optimizaiton,the proposed ML models can give one motor-drive design point up to 0.044 s while it takes more than 1.5 mins for the used simulation-based models.

Generic functional modelling of multi-pulse auto-transformer rectifier units for more-electric aircraft applications

The Auto-Transformer Rectifier Unit（ATRU） is one preferred solution for high-power AC/DC power conversion in aircraft. This is mainly due to its simple structure, high reliability and reduced k VA ratings. Indeed, the ATRU has become a preferred AC/DC solution to supply power to the electric environment control system on-board future aircraft. In this paper, a general modelling method for ATRUs is introduced. The developed model is based on the fact that the DC voltage and current are strongly related to the voltage and current vectors at the AC terminals of ATRUs. In this paper, we carry on our research in modelling symmetric 18-pulse ATRUs and develop a generic modelling technique. The developed generic model can study not only symmetric but also asymmetric ATRUs. An 18-pulse asymmetric ATRU is used to demonstrate the accuracy and efficiency of the developed model by comparing with corresponding detailed switching SABER models provided by our industrial partner. The functional models also allow accelerated and accurate simulations and thus enable whole-scale more-electric aircraft electrical power system studies in the future.

Neural Network aided PMSM multi-objective design and optimization for more-electric aircraft applications

This study uses the Neural Network(NN)technique to optimize design of surfacemounted Permanent Magnet Synchronous Motors(PMSMs)for More-Electric Aircraft(MEA)applications.The key role of NN is to provide dedicated correction factors for the analytical PMSM mass and loss estimation within the entire design space.Based on that,a globally optimal design can be quickly obtained.Matching the analytical estimation with Finite-Element Analysis(FEA)is the main research target of training the NN.Conventional analytical formulae serve as the basis of this study,but they are prone to loss accuracy(especially for a large design space)due to their assumptions and simplifications.With the help of the trained NNs,the analytical motor model can give an estimation as accurate as the FEA but with super less time during the optimization process.The Average Correction Factor(ACF)approach is regarded as the comparison method to demonstrate the excellent performance of the proposed NN model.Furthermore,a NN aided three-stage-sevenstep optimization methodology is proposed.Finally,a Pole-10-Slot-12 PMSM case study is given to demonstrate the feasibility and gain of the NN aided multi-objective optimization approach.In this case,the NN aided analytical model can generate one motor design in 0.04 s while it takes more than 1 min for the used FEA model.