Design of Fault-Tolerant Dual Three-Phase Winding PMSM for Helicopter Landing Gear EMA

Giangrande, Paolo; Madonna, Vincenzo; Nuzzo, Stefano; Galea, Michael

doi:10.1109/esars-itec.2018.8607684

Cited by 29 publications

(25 citation statements)

References 25 publications

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“…The test specimen is a twisted pair wire (round enameled magnet wire) with a thermal class of 220 • C, wire diameter The chosen specimen's arrangement is done according to the ASTM standards D2307 [15] since it is aimed for a low voltage electrical machine [39].…”

Section: Experimental Test Setup a Test Specimenmentioning

confidence: 99%

The Role of Neural Networks in Predicting the Thermal Life of Electrical Machines

et al. 2020

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For a continuous mode of operation, insulating material in an electrical machine is subject to constant thermal, electrical, mechanical and environmental stresses where thermal stress is a major cause of gradual insulation deterioration, which leads to ultimate winding failure. To guarantee a satisfactory lifetime, electrical machines are designed to operate winding temperatures well below their thermal class, which results in an oversized design. Standard methods for thermal lifetime evaluation of electrical machines are based on accelerated aging tests that require several months of testing. This paper proposes an alternative approach relying on a supervised neural network that significantly shortens the time demanded by accelerated aging tests for thermal lifetime evaluation of electrical machines. The supervised neural network is based on a feedforward neural network trained with Bayesian Regularisation Backpropagation (BRP) algorithm. The network predicts the wire insulation resistance with respect to its aging time at aging temperatures of 250 • C, 270 • C and 290 • C, which reveals a good match of prediction outcomes against the experimental findings. The mean time-to-failure at each aging temperature is extracted using the Weibull probability plot in order to compare the Arrhenius curves for both conventional and proposed method and a relative error of 0.125% is achieved in terms of their temperature indexes. In addition, the analysis shows a time saving of 1680 hours (57% time saved of experimental test procedure) when the thermal life of the insulating material is predicted using BRP neural network. INDEX TERMS Neural network, aging time, thermal life of insulation, accelerated lifetime test.

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Section: Experimental Test Setup a Test Specimenmentioning

confidence: 99%

The Role of Neural Networks in Predicting the Thermal Life of Electrical Machines

et al. 2020

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“…In this work, the focus has been put on EMs working under short-term overload conditions. This operating mode is ordinary in the electromechanical actuator for aerospace applications [35,36].…”

Section: Application Of the Studymentioning

confidence: 99%

Evaluation of strand‐to‐strand capacitance and dissipation factor in thermally aged enamelled coils for low‐voltage electrical machines

Madonna

Giangrande

Galea

2019

IET Science, Measurement & Technology

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“…outer rotor, 2.) 2x3phase fractional slot concentrated windings arrangement with 30° displacement angle [11], and 3.) Halbach array configuration employing high-temperature resistant Samarium-Cobalt permanent magnets (ψ pm =0.654Wb).…”

Section: Description and Modelling Of The Et Systemmentioning

confidence: 99%

Analysis of Energy Storage System Requirements for Aircraft Electric Taxiing Operations

Lukic

Giangrande

Klumpner

et al. 2019

IECON 2019 - 45th Annual Conference of the IEEE Industrial Electronics Society

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This paper focuses on evaluating the energy and power requirements of a specific aircraft on-board electric taxiing (ET) system. The developed model of the investigated system is used to determine the requisites for a typical taxiing profile mission of a Boeing 737-400. Besides the derivation of the specifications, the comparison of batteries and electrochemical capacitors is outlined in the light of viable candidates for a local energy storage system (LESS). It is estimated that LESS should be sized for capacity of 19kWh and peak power of 81kW. The paper is concluded with a comparison and discussion on LESS topologies.

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Design of Fault-Tolerant Dual Three-Phase Winding PMSM for Helicopter Landing Gear EMA

Cited by 29 publications

References 25 publications

The Role of Neural Networks in Predicting the Thermal Life of Electrical Machines

The Role of Neural Networks in Predicting the Thermal Life of Electrical Machines

Evaluation of strand‐to‐strand capacitance and dissipation factor in thermally aged enamelled coils for low‐voltage electrical machines

Analysis of Energy Storage System Requirements for Aircraft Electric Taxiing Operations

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