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In scenarios involving parallel transformers with disparate ratings, developing per-unit models requires accommodating off-nominal turns ratios. This situation arises when the selected base voltages are not proportional to the transformer’s voltage ratings. Consider a transformer where the rated voltages are related by the term a. If the chosen voltage bases satisfy a relationship involving term b, term c is defined as the ratio of these bases. This ratio is then substituted into the...
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Related Experiment Video

Updated: Mar 20, 2026

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
11:53

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

Published on: December 9, 2012

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Optimized parameter estimation of solar PV models using an improved red-tailed hawk algorithm.

Pankaj Sharma1,2, Asmita Ajay Rathod1,2, Shubhi Shukla1

  • 1School of Electrical Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India.

Scientific Reports
|March 19, 2026
PubMed
Summary

A new Improved Red-tailed Hawk (IRTHA) Algorithm optimizes photovoltaic (PV) systems. This metaheuristic method enhances accuracy and efficiency in PV parameter estimation, outperforming existing algorithms.

Keywords:
Improved red-tailed hawk algorithmMeta-heuristic algorithmsSolar PVStatistical analysis

Related Experiment Videos

Last Updated: Mar 20, 2026

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
11:53

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

Published on: December 9, 2012

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Area of Science:

  • Renewable Energy Engineering
  • Computational Intelligence
  • Optimization Algorithms

Background:

  • Accurate modeling of photovoltaic (PV) systems is crucial for efficient solar energy utilization.
  • Existing metaheuristic optimization methods face challenges in balancing exploration and exploitation for complex PV parameter estimation.

Purpose of the Study:

  • To introduce the Improved Red-tailed Hawk (IRTHA) Algorithm, a novel metaheuristic optimization technique.
  • To enhance the convergence efficacy and accuracy of optimization outcomes for PV models.
  • To adaptively balance exploration and exploitation in optimization processes.

Main Methods:

  • Development of the IRTHA Algorithm inspired by red-tailed hawk predatory behavior.
  • Integration of dynamic adjustment using nonlinear decay and chaotic mapping for enhanced search capabilities.
  • Application and validation of IRTHA on various single, double, and triple diode models of diverse PV cells and modules.

Main Results:

  • IRTHA achieved significantly lower Root Mean Square Error (RMSE) values across multiple PV models compared to other advanced metaheuristic techniques.
  • Statistical analyses, including non-parametric tests and boxplots, confirmed IRTHA's robustness and computational efficiency.
  • Demonstrated superior performance in PV system modeling and parameter estimation.

Conclusions:

  • The IRTHA Algorithm offers a robust and computationally efficient solution for PV system parameter estimation.
  • IRTHA effectively balances exploration and exploitation, leading to improved accuracy and convergence.
  • The proposed method shows exceptional potential for advancing solar PV applications.