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Mr. Elia Brescia
Politecnico di Bari

Basic Info


Research Keywords & Expertise

0 Electric Drives
0 Software Design
0 Finite Element Analysis (FEA)
0 Permanent Magnet Synchronous Machine
0 Simulation and Optimization

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Short Biography

Elia Brescia received the bachelor’s degree and the master’s degree from the Polytechnic of Bari, Bari, Italy, in 2015 and 2018, respectively. Currently he is a PhD student and his research interests cover the design and control 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.

Journal article
Published: 07 September 2020 in Energies
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This paper proposes a new variable structure control scheme for a variable-speed, fixed-pitch ducted wind turbine, equipped with an annular, brushless permanent-magnet synchronous generator, considering a back-to-back power converter topology. The purpose of this control scheme is to maximise the aerodynamic power over the entire wind speed range, considering the mechanical safety limits of the ducted wind turbine. The ideal power characteristics are achieved with the design of control laws aimed at performing the maximum power point tracking control in the low wind speeds region, and the constant speed, power, and torque control in the high wind speed region. The designed control laws utilize a Luenberger observer for the estimation of the aerodynamic torque and a shallow neural network for wind speed estimation. The effectiveness of the proposed method was verified through tests in a laboratory setup. Moreover, a comparison with other solutions from the literature allowed us to better evaluate the performances achieved and to highlight the originality of the proposed control scheme.

ACS Style

Diego Calabrese; Gioacchino Tricarico; Elia Brescia; Giuseppe Leonardo Cascella; Vito Giuseppe Monopoli; Francesco Cupertino. Variable Structure Control of a Small Ducted Wind Turbine in the Whole Wind Speed Range Using a Luenberger Observer. Energies 2020, 13, 4647 .

AMA Style

Diego Calabrese, Gioacchino Tricarico, Elia Brescia, Giuseppe Leonardo Cascella, Vito Giuseppe Monopoli, Francesco Cupertino. Variable Structure Control of a Small Ducted Wind Turbine in the Whole Wind Speed Range Using a Luenberger Observer. Energies. 2020; 13 (18):4647.

Chicago/Turabian Style

Diego Calabrese; Gioacchino Tricarico; Elia Brescia; Giuseppe Leonardo Cascella; Vito Giuseppe Monopoli; Francesco Cupertino. 2020. "Variable Structure Control of a Small Ducted Wind Turbine in the Whole Wind Speed Range Using a Luenberger Observer." Energies 13, no. 18: 4647.

Conference paper
Published: 23 August 2020 in 2020 International Conference on Electrical Machines (ICEM)
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This paper proposes an innovative design method for the cogging torque suppression of permanent magnet machines with a segmented stator core. This method is based on the design of the shape of the stator tooth tips through a quasi-binary optimization using a genetic algorithm and iterative finite element analysis. The method has been applied to an annular permanent magnet synchronous machine with a segmented stator core for a low power wind generator. The solutions achieved by the optimization have been validated through accurate finite element analysis. The optimized machine shows a reduction of the cogging torque by 94% in comparison with the basic one.

ACS Style

E. Brescia; M. Palmieri; G. L. Cascella; F. Cupertino. Optimal Tooth Tips Design for Cogging Torque Suppression of Permanent Magnet Machines with a Segmented Stator Core. 2020 International Conference on Electrical Machines (ICEM) 2020, 1, 1930 -1936.

AMA Style

E. Brescia, M. Palmieri, G. L. Cascella, F. Cupertino. Optimal Tooth Tips Design for Cogging Torque Suppression of Permanent Magnet Machines with a Segmented Stator Core. 2020 International Conference on Electrical Machines (ICEM). 2020; 1 ():1930-1936.

Chicago/Turabian Style

E. Brescia; M. Palmieri; G. L. Cascella; F. Cupertino. 2020. "Optimal Tooth Tips Design for Cogging Torque Suppression of Permanent Magnet Machines with a Segmented Stator Core." 2020 International Conference on Electrical Machines (ICEM) 1, no. : 1930-1936.