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Dr. Donatello Costantino
Politecnico di Bari

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0 Electric Drives
0 control strategy
0 control & diagnostic systems
0 control algorithm
0 identification algorithm

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Short Biography

Donatello Costantino received the bachelor’s degree and the master’s degree in Automation engineering from the Polytechnic of Bari, Bari, Italy, in 2016 and 2020, respectively. Currently he is a graduate research fellow and his research interests cover the control and parameter identification of permanent magnet electrical machines.

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Journal article
Published: 09 July 2021 in Sensors
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Parameter identification of permanent magnet synchronous machines (PMSMs) represents a well-established research area. However, parameter estimation of multiple running machines in large-scale applications has not yet been investigated. In this context, a flexible and automated approach is required to minimize complexity, costs, and human interventions without requiring machine information. This paper proposes a novel identification strategy for surface PMSMs (SPMSMs), highly suitable for large-scale systems. A novel multistep approach using measurement data at different operating conditions of the SPMSM is proposed to perform the parameter identification without requiring signal injection, extra sensors, machine information, and human interventions. Thus, the proposed method overcomes numerous issues of the existing parameter identification schemes. An IoT/cloud architecture is designed to implement the proposed multistep procedure and massively perform SPMSM parameter identifications. Finally, hardware-in-the-loop results show the effectiveness of the proposed approach.

ACS Style

Elia Brescia; Donatello Costantino; Federico Marzo; Paolo Massenio; Giuseppe Cascella; David Naso. Automated Multistep Parameter Identification of SPMSMs in Large-Scale Applications Using Cloud Computing Resources. Sensors 2021, 21, 4699 .

AMA Style

Elia Brescia, Donatello Costantino, Federico Marzo, Paolo Massenio, Giuseppe Cascella, David Naso. Automated Multistep Parameter Identification of SPMSMs in Large-Scale Applications Using Cloud Computing Resources. Sensors. 2021; 21 (14):4699.

Chicago/Turabian Style

Elia Brescia; Donatello Costantino; Federico Marzo; Paolo Massenio; Giuseppe Cascella; David Naso. 2021. "Automated Multistep Parameter Identification of SPMSMs in Large-Scale Applications Using Cloud Computing Resources." Sensors 21, no. 14: 4699.

Journal article
Published: 29 March 2021 in Energies
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Permanent magnet machines with segmented stator cores are affected by additional harmonic components of the cogging torque which cannot be minimized by conventional methods adopted for one-piece stator machines. In this study, a novel approach is proposed to minimize the cogging torque of such machines. This approach is based on the design of multiple independent shapes of the tooth tips through a topological optimization. Theoretical studies define a design formula that allows to choose the number of independent shapes to be designed, based on the number of stator core segments. Moreover, a computationally-efficient heuristic approach based on genetic algorithms and artificial neural network-based surrogate models solves the topological optimization and finds the optimal tooth tips shapes. Simulation studies with the finite element method validates the design formula and the effectiveness of the proposed method in suppressing the additional harmonic components. Moreover, a comparison with a conventional heuristic approach based on a genetic algorithm directly coupled to finite element analysis assesses the superiority of the proposed approach. Finally, a sensitivity analysis on assembling and manufacturing tolerances proves the robustness of the proposed design method.

ACS Style

Elia Brescia; Donatello Costantino; Paolo Massenio; Vito Monopoli; Francesco Cupertino; Giuseppe Cascella. A Design Method for the Cogging Torque Minimization of Permanent Magnet Machines with a Segmented Stator Core Based on ANN Surrogate Models. Energies 2021, 14, 1880 .

AMA Style

Elia Brescia, Donatello Costantino, Paolo Massenio, Vito Monopoli, Francesco Cupertino, Giuseppe Cascella. A Design Method for the Cogging Torque Minimization of Permanent Magnet Machines with a Segmented Stator Core Based on ANN Surrogate Models. Energies. 2021; 14 (7):1880.

Chicago/Turabian Style

Elia Brescia; Donatello Costantino; Paolo Massenio; Vito Monopoli; Francesco Cupertino; Giuseppe Cascella. 2021. "A Design Method for the Cogging Torque Minimization of Permanent Magnet Machines with a Segmented Stator Core Based on ANN Surrogate Models." Energies 14, no. 7: 1880.