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Related Concept Videos

Multiple Voltage Sources01:25

Multiple Voltage Sources

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Generally, a single battery is not enough to power some devices. In such cases, batteries can be combined in two ways: in series or in parallel.
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A Geographic Information System (GIS) combines specialized software and hardware to effectively manage, analyze, and present spatial and related data. GIS software includes critical functionalities such as a user interface for easy navigation, database management tools for handling spatial and attribute data, and data retrieval features for efficient access. Analytical tools transform raw data into insights, while display functions produce maps and reports in various formats for effective...
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Preparing a Celadonite Electron Source and Estimating Its Brightness
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Data for resistance and inductance estimation within a voltage source inverter.

Diego Aldana1, Yamisleydi Salgueiro1, Colin Bellinger2

  • 1Facultad de Ingeniería, Universidad de Talca, Chile.

Data in Brief
|July 24, 2019
PubMed
Summary

This study presents datasets for voltage source inverters (VSI) using model predictive control (MPC). The data supports developing nonintrusive models to predict VSI resistance and inductance under varying loads.

Keywords:
High dimensional machine learningNonintrusive monitoringVoltage source inverter data

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Area of Science:

  • Electrical Engineering
  • Renewable Energy Systems
  • Control Systems

Background:

  • Power converters, specifically Voltage Source Inverters (VSI), are crucial for integrating renewable energy sources like photovoltaics.
  • Model Predictive Control (MPC) is utilized in VSIs to manage power conversion, requiring accurate parameter estimation.
  • Variable loads in motor drives necessitate precise adjustments to VSI control parameters like resistance and inductance.

Purpose of the Study:

  • To generate and describe datasets for a VSI incorporating MPC.
  • To provide data supporting the development of nonintrusive models for parameter prediction.
  • To facilitate research into predicting VSI resistance and inductance under diverse operating conditions.

Main Methods:

  • Generation of four datasets, each with 399 instances.
  • Simulations involving variations in inductance (continuous and discrete) for VSI models.
  • Simulations involving variations in resistance (continuous and discrete) for VSI models.

Main Results:

  • Creation of comprehensive datasets detailing VSI behavior under varied inductance and resistance.
  • The datasets capture both continuous and discrete variations of these essential parameters.
  • Data is structured to enable the training and validation of predictive models.

Conclusions:

  • The generated data is vital for advancing nonintrusive modeling techniques for VSIs.
  • Accurate prediction of resistance and inductance can enhance VSI performance and efficiency.
  • This work provides a foundation for developing more robust and adaptive VSI control strategies.