Accurate estimation of the internal temperatures of electric machines is critical to increasing their power density and reliability since key temperatures,such as magnet temperature,are often difficult to measure.This...Accurate estimation of the internal temperatures of electric machines is critical to increasing their power density and reliability since key temperatures,such as magnet temperature,are often difficult to measure.This work presents a new machine learning based modelling approach,incorporating novel physically informed feature engineering,which achieves best-in-class accuracy and reduced training time.The different features introduced are proportional to sources of machine losses and require no prior knowledge of the machine,hence the models are completely data driven.Evaluation using a standard experimental dataset shows that modelling errors can be reduced by up to 82.5%,resulting in the lowest mean squared error recorded in the literature of 2.40 K^(2).Additionally,models can be trained with less training data and have lower sensitivity to data quality.Specif-ically,it was possible to train a loss enhanced multilayer perceptron model to a mean squared error<5 K^(2) with 90 h of training data,and an enhanced ordinary least squares model with just 60 h to the same criteria.The inference time of the model can be 1–2 orders of magnitude faster than competing models and requires no time to optimise hyperparameters,compared to weeks or months for other state-of-the-art prediction methods.These results are highly important for enabling low-cost real-time temperature monitoring of electric machines to improve operational efficiency,safety,reliability,and power density.展开更多
Real-time simulation-based validation plays an essential role in the early stage development of electric traction systems.The high-fidelity real-time simulation relies on the accurate modeling of the power electronics...Real-time simulation-based validation plays an essential role in the early stage development of electric traction systems.The high-fidelity real-time simulation relies on the accurate modeling of the power electronics and motors and usually represents the nonlinear characteristics as many as possible while obeying the computing time constraints.In this paper,an artificial neural network(ANN)aided modeling approach is proposed for the field-programmable gate array(FPGA)-based real-time simulation to deal with the nonlinearities in permanent magnet synchronous motor(PMSM)and its drive.With the help of the ANN,the switches power losses,and the PMSM nonlinear flux linkage and electromagnetic torque can be modeled.An electro-thermal model of inverters and the finite element analysis(FEA)-based PMSM model are thus enabled in the FPGA-based real-time simulation,which can significantly improve the performances of the real-time simulation-based test bench.A two-level inverter fed PMSM drive system is modeled using the ANN-aided modeling approach and simulated in the National Instrument PXIe FlexRIO FPGA real-time system.The accuracy and effectiveness of the proposed approaches are tested and validated by comparing the results with the offline simulation tools.展开更多
INSITE(Integrated System for Information Technology and Engineering)软件是哈里伯顿公司支持其随钻测井服务的地面系统,其核心部分是一个庞大的开放式数据库(ODBC)连接的数据库系统。通过探索INSITE软件的隐藏功能,可以在广域网和...INSITE(Integrated System for Information Technology and Engineering)软件是哈里伯顿公司支持其随钻测井服务的地面系统,其核心部分是一个庞大的开放式数据库(ODBC)连接的数据库系统。通过探索INSITE软件的隐藏功能,可以在广域网和局域网内让全球INSITE工作站实时连接在一起,在任何地方都可以共享到这些数据库,从而实现24小时的实时监控,为现场作业提供强大的实时支持。主要介绍的随钻测井、测量相关模块的数据库原理。同时通过现场对隐藏功能的开发应用,实现现场作业难题的解决,减少因故障造成的经济损失。展开更多
文摘Accurate estimation of the internal temperatures of electric machines is critical to increasing their power density and reliability since key temperatures,such as magnet temperature,are often difficult to measure.This work presents a new machine learning based modelling approach,incorporating novel physically informed feature engineering,which achieves best-in-class accuracy and reduced training time.The different features introduced are proportional to sources of machine losses and require no prior knowledge of the machine,hence the models are completely data driven.Evaluation using a standard experimental dataset shows that modelling errors can be reduced by up to 82.5%,resulting in the lowest mean squared error recorded in the literature of 2.40 K^(2).Additionally,models can be trained with less training data and have lower sensitivity to data quality.Specif-ically,it was possible to train a loss enhanced multilayer perceptron model to a mean squared error<5 K^(2) with 90 h of training data,and an enhanced ordinary least squares model with just 60 h to the same criteria.The inference time of the model can be 1–2 orders of magnitude faster than competing models and requires no time to optimise hyperparameters,compared to weeks or months for other state-of-the-art prediction methods.These results are highly important for enabling low-cost real-time temperature monitoring of electric machines to improve operational efficiency,safety,reliability,and power density.
基金This work was supported by European Commission H2020 grant PANDA(grant no.H2020-LC-GV-2018),EU grant no.824256.
文摘Real-time simulation-based validation plays an essential role in the early stage development of electric traction systems.The high-fidelity real-time simulation relies on the accurate modeling of the power electronics and motors and usually represents the nonlinear characteristics as many as possible while obeying the computing time constraints.In this paper,an artificial neural network(ANN)aided modeling approach is proposed for the field-programmable gate array(FPGA)-based real-time simulation to deal with the nonlinearities in permanent magnet synchronous motor(PMSM)and its drive.With the help of the ANN,the switches power losses,and the PMSM nonlinear flux linkage and electromagnetic torque can be modeled.An electro-thermal model of inverters and the finite element analysis(FEA)-based PMSM model are thus enabled in the FPGA-based real-time simulation,which can significantly improve the performances of the real-time simulation-based test bench.A two-level inverter fed PMSM drive system is modeled using the ANN-aided modeling approach and simulated in the National Instrument PXIe FlexRIO FPGA real-time system.The accuracy and effectiveness of the proposed approaches are tested and validated by comparing the results with the offline simulation tools.
文摘INSITE(Integrated System for Information Technology and Engineering)软件是哈里伯顿公司支持其随钻测井服务的地面系统,其核心部分是一个庞大的开放式数据库(ODBC)连接的数据库系统。通过探索INSITE软件的隐藏功能,可以在广域网和局域网内让全球INSITE工作站实时连接在一起,在任何地方都可以共享到这些数据库,从而实现24小时的实时监控,为现场作业提供强大的实时支持。主要介绍的随钻测井、测量相关模块的数据库原理。同时通过现场对隐藏功能的开发应用,实现现场作业难题的解决,减少因故障造成的经济损失。
