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A new computing approach for power signal modeling using fractional adaptive algorithms.

Naveed Ishtiaq Chaudhary1, Syed Zubair1, Muhammad Asif Zahoor Raja2

  • 1Department of Electronic Engineering, International Islamic University, Islamabad, Pakistan.

ISA Transactions
|March 28, 2017
PubMed
Summary

This study introduces fractional adaptive signal processing (FrASP) for accurate harmonic parameter estimation in power signals. The novel FrASP algorithms demonstrate reliable performance across various scenarios, enhancing power system modeling.

Keywords:
Fractional adaptive algorithmsNonlinear adaptive strategiesParameter estimationPower signalSignal modeling

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Area of Science:

  • Electrical Engineering
  • Signal Processing
  • Adaptive Systems

Background:

  • Accurate estimation of harmonic parameters is crucial for effective signal modeling in power supply systems.
  • Existing methods may have limitations in handling complex power signal dynamics.

Purpose of the Study:

  • To develop and evaluate novel fractional adaptive signal processing (FrASP) algorithms for parameter identification in power signals.
  • To assess the performance of these algorithms under varying conditions, including step size and noise levels.

Main Methods:

  • Design of FrASP algorithms based on generalized least mean square (LMS) adaptive strategies.
  • Simulation of power signal scenarios to evaluate algorithm performance.
  • Analysis using performance metrics such as mean square error, variance account for, and Nash Sutcliffe efficiency.

Main Results:

  • The proposed fractional adaptive schemes demonstrated reliable and effective parameter estimation.
  • Performance was validated across different scenarios, showing robustness to step size and noise variations.
  • Key performance measures confirmed the efficacy of the developed methods.

Conclusions:

  • The developed FrASP algorithms offer a reliable approach for harmonic parameter estimation in power systems.
  • These methods contribute to improved signal modeling and analysis in electrical power engineering.
  • The study validates the effectiveness of fractional adaptive filtering for power signal processing.