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Frequency converter as a node for edge computing of big data, related to drive efficiency, in industrial internet of things

Research output: Contribution to journal › Article › peer-review

Abstract

The article presents a method of generating key performance indicators related to electric motor energy efficiency on the basis of Big Data gathered and processed in a frequency converter. The authors proved that using the proposed solution, it is possible to specify the relation between the control mode of an electric drive and the control quality-energy consumption ratio in the start-up phase as well as in the steady operation with various mechanical loads. The tests were carried out on a stand equipped with two electric motors (one driving, the other used to apply the load by adjusting the parameters of the built-in brake). The measurements were made in two load cases, for motor control modes available in industrially applied frequency converters (scalar V/f, vector Voltage flux control without encoder, vector voltage flux control with encoder, vector current flux control, and vector current flux control with torque control). During the experiments, values of the current intensities (active and output), the actual frequency value, IxT utilization factor, relative torque, and the current rotational speed were measured and processed. Based on the data, the level of energy efficiency was determined for various control modes.

Original languageEnglish
Article number9784
JournalApplied Sciences (Switzerland)
Volume11
Issue number21
DOIs
Publication statusPublished - 1 Nov 2021

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 7 - Affordable and Clean Energy
    SDG 7 Affordable and Clean Energy
  2. SDG 9 - Industry, Innovation, and Infrastructure
    SDG 9 Industry, Innovation, and Infrastructure

Keywords

  • Big Data
  • Dashboard
  • Edge computing
  • Electric drive
  • Electric motor control
  • Energy efficiency
  • Frequency converter
  • Industrial Internet of Things
  • KPI
  • Key performance indicators

ASJC Scopus subject areas

  • General Materials Science
  • Instrumentation
  • General Engineering
  • Process Chemistry and Technology
  • Computer Science Applications
  • Fluid Flow and Transfer Processes

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