Abstract This paper presents the results of simulation research of an off-line-trained, feedforward neural-network-based state estimator. The investigated system is the mechanical part of an electrical drive characterised by elastic coupling with a working machine, modelled as a dual-mass system. The aim of the research was to find a set of neural network structures giving useful and repeatable results of the estimation. The mechanical resonance frequency of the system has been adopted at the level of 9.3-10.3 Hz. The selected state variables of the mechanical system are load, speed and stiffness torque of the shaft.
Neural State Estimator for Complex Mechanical Part of Electrical Drive: Neural Network Size and Performance of State Estimation
Power Electronics and Drives ; 3 , 1
2018
Aufsatz (Zeitschrift)
Englisch
BKL: | 53.35 Stromrichter, Transformatoren / 53.33$jElektrische Maschinen und Antriebe / 53.33 Elektrische Maschinen und Antriebe / 53.35$jStromrichter$jTransformatoren |
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