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Related Experiment Video

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Field data-based mathematical modeling by Bode equations and vector fitting algorithm for renewable energy

A H Sabry1, W Z W Hasan2, M Z A Ab Kadir2

  • 1Department of Control and Automation, Faculty of Engineering, UPM, Serdang, Malaysia.

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|January 20, 2018
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Summary
This summary is machine-generated.

This study introduces a modified vector fitting (VF) algorithm for accurate power generation modeling. The new parametric technique enhances curve fitting for wind, solar, and demand power data, improving system evaluation.

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Area of Science:

  • Electrical Engineering
  • Computational Mathematics
  • Data Science

Background:

  • Power system parameters fluctuate due to load variations and environmental factors, necessitating accurate time-varying mathematical models.
  • Curve modeling is crucial for power generation performance evaluation, monitoring, and forecasting, with existing numerical techniques facing limitations with empirical data.
  • Accurate modeling of power generation curves, including wind, solar, and demand, is essential for grid stability and efficient energy management.

Purpose of the Study:

  • To propose a novel modified parametric technique for power system modeling.
  • To extend the applicability of the vector fitting (VF) algorithm beyond frequency-domain analysis to encompass all power curves.
  • To enhance the accuracy and robustness of empirical data fitting for power generation modeling.

Main Methods:

  • A modified parametric technique based on Bode plot equations and the vector fitting (VF) algorithm was developed.
  • The methodology involves fitting experimental data points to derive system modeling equations.
  • The approach was validated through four case studies, comparing its performance against established numerical methods.

Main Results:

  • The modified VF algorithm demonstrated significant improvements in data fitting accuracy, evidenced by minimal Root Mean Square Error (RMSE).
  • The method effectively models irregular and randomly shaped power generation data, outperforming common fitting techniques.
  • The enhanced VF algorithm provides state-space or transfer function models applicable to frequency-domain data estimation algorithms.

Conclusions:

  • The proposed modified VF algorithm offers a robust and versatile solution for power system modeling and forecasting.
  • This parametric technique enhances the accuracy of empirical data fitting, particularly for variable power sources like wind and solar.
  • The method's ability to handle complex data shapes broadens its application in power system analysis and control.