Pareto Analysis of Electro-Mechanical Variables in Predictive Control of Drives

Variable speed drives are often controlled by a double-loop scheme in which a proportional integral controller takes on the speed loop. The tuning of this loop is a complex job. In most cases just mechanical variables are considered for tuning. This paper presents a new Pareto analysis incorporating mechanical and electrical variables. A state of the art finite state model predictive controller is used for stator current control. The analysis is performed using experimental data from a five-phase induction motor and considers considering commonly found performance indicators derived from experimental data. The results show undocumented connections between those performance indicators. The analysis not only helps in PI tuning but, more importantly, prompts for a revision of the methods usually utilized to report performance enhancements of new methods.

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Publication Details

Journal
Machines
Published
2025-02-15
DOI
https://doi.org/10.3390/machines13020150
Primary Topic
Multilevel Inverters and Converters
Type
article
Field-Weighted Citation Impact
0.00

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article

Pareto Analysis of Electro-Mechanical Variables in Predictive Control of Drives

Manuel G. Satué, Manuel R. Arahal, Manuel G. Ortega
Machines
Multilevel Inverters and Converters
article

Pareto Analysis of Electro-Mechanical Variables in Predictive Control of Drives

Manuel G. Satué, Manuel R. Arahal, Manuel G. Ortega
article en

Abstract

Variable speed drives are often controlled by a double-loop scheme in which a proportional integral controller takes on the speed loop. The tuning of this loop is a complex job. In most cases just mechanical variables are considered for tuning. This paper presents a new Pareto analysis incorporating mechanical and electrical variables. A state of the art finite state model predictive controller is used for stator current control. The analysis is performed using experimental data from a five-phase induction motor and considers considering commonly found performance indicators derived from experimental data. The results show undocumented connections between those performance indicators. The analysis not only helps in PI tuning but, more importantly, prompts for a revision of the methods usually utilized to report performance enhancements of new methods.

MachinesVol. 13(2)
Universidad de Sevilla (ES)
Ministerio de Ciencia, Innovación y Universidades, European Regional Development Fund, Agencia Estatal de Investigación
Openalex Percentile: Top 98%
Multilevel Inverters and Converters
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Pareto Analysis of Electro-Mechanical Variables in Predictive Control of Drives — Manuel G. Satué, Manuel R. Arahal, et al. · Machines (2025) | TGRS Research Map | TGRS