Automated Multistep Parameter Identification of SPMSMs in Large-Scale Applications Using Cloud Computing Resources
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...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-07-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/21/14/4699 |
| _version_ | 1797526042658734080 |
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| author | Elia Brescia Donatello Costantino Federico Marzo Paolo Roberto Massenio Giuseppe Leonardo Cascella David Naso |
| author_facet | Elia Brescia Donatello Costantino Federico Marzo Paolo Roberto Massenio Giuseppe Leonardo Cascella David Naso |
| author_sort | Elia Brescia |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T09:24:32Z |
| format | Article |
| id | doaj.art-3c627853196842758a13aae2051c4e7d |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T09:24:32Z |
| publishDate | 2021-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-3c627853196842758a13aae2051c4e7d2023-11-22T04:54:46ZengMDPI AGSensors1424-82202021-07-012114469910.3390/s21144699Automated Multistep Parameter Identification of SPMSMs in Large-Scale Applications Using Cloud Computing ResourcesElia Brescia0Donatello Costantino1Federico Marzo2Paolo Roberto Massenio3Giuseppe Leonardo Cascella4David Naso5Department of Electrical Engineering and Information Technology, Politecnico di Bari, 70126 Bari, ItalyDepartment of Electrical Engineering and Information Technology, Politecnico di Bari, 70126 Bari, ItalyDepartment of Electrical Engineering and Information Technology, Politecnico di Bari, 70126 Bari, ItalyDepartment of Electrical Engineering and Information Technology, Politecnico di Bari, 70126 Bari, ItalyDepartment of Electrical Engineering and Information Technology, Politecnico di Bari, 70126 Bari, ItalyDepartment of Electrical Engineering and Information Technology, Politecnico di Bari, 70126 Bari, ItalyParameter 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.https://www.mdpi.com/1424-8220/21/14/4699adaline neural networkcloud computinginternet of thingsparameter identificationpermanent magnet synchronous machinesR-statistic |
| spellingShingle | Elia Brescia Donatello Costantino Federico Marzo Paolo Roberto Massenio Giuseppe Leonardo Cascella David Naso Automated Multistep Parameter Identification of SPMSMs in Large-Scale Applications Using Cloud Computing Resources Sensors adaline neural network cloud computing internet of things parameter identification permanent magnet synchronous machines R-statistic |
| title | Automated Multistep Parameter Identification of SPMSMs in Large-Scale Applications Using Cloud Computing Resources |
| title_full | Automated Multistep Parameter Identification of SPMSMs in Large-Scale Applications Using Cloud Computing Resources |
| title_fullStr | Automated Multistep Parameter Identification of SPMSMs in Large-Scale Applications Using Cloud Computing Resources |
| title_full_unstemmed | Automated Multistep Parameter Identification of SPMSMs in Large-Scale Applications Using Cloud Computing Resources |
| title_short | Automated Multistep Parameter Identification of SPMSMs in Large-Scale Applications Using Cloud Computing Resources |
| title_sort | automated multistep parameter identification of spmsms in large scale applications using cloud computing resources |
| topic | adaline neural network cloud computing internet of things parameter identification permanent magnet synchronous machines R-statistic |
| url | https://www.mdpi.com/1424-8220/21/14/4699 |
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