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The maximum power flow for lossy transmission lines is derived using ABCD parameters in phasor form. These parameters create a matrix relationship between the sending-end and receiving-end voltages and currents, allowing the determination of the receiving-end current. This relationship facilitates calculating the complex power delivered to the receiving end, from which real and reactive power components are derived.
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An Improved Elk Herd Optimization Algorithm for Maximum Power Point Tracking in Photovoltaic Systems Under Partial

Gang Zheng1, Wenchang Wei1, Heming Jia2

  • 1College of Computer and Control Engineering, Northeast Forestry University, Harbin 150040, China.

Biomimetics (Basel, Switzerland)
|August 27, 2025
PubMed
Summary
This summary is machine-generated.

Traditional maximum power point tracking (MPPT) struggles with partial shading. An improved elk herd optimization (IEHO) algorithm rapidly finds the global maximum power point, boosting photovoltaic system efficiency under varying conditions.

Keywords:
improved elk herd optimizationmaximum power point trackingpartial shading conditionphotovoltaic system

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Area of Science:

  • Renewable Energy Systems
  • Photovoltaic Power Conversion
  • Optimization Algorithms

Background:

  • Partial shading conditions (PSCs) in photovoltaic (PV) systems create multiple peaks in power-voltage characteristics.
  • Traditional maximum power point tracking (MPPT) algorithms often get trapped in local optima, reducing energy conversion efficiency.
  • Rapidly and effectively locating the global maximum power point under complex environmental conditions is crucial for PV performance.

Purpose of the Study:

  • To propose an improved elk herd optimization (IEHO) algorithm for rapid global maximum power point tracking (MPPT) in PV systems under various weather conditions.
  • To enhance MPPT performance and energy conversion efficiency in PV systems operating under partial shading.

Main Methods:

  • Developed an improved elk herd optimization (IEHO) algorithm incorporating a position update mechanism guided by predation risk probability.
  • Introduced a triangle walk strategy to improve the algorithm's ability to escape local optima.
  • Implemented a memory-guided redirection strategy to optimize convergence speed by skipping redundant historical duty cycle calculations.

Main Results:

  • The IEHO algorithm demonstrated superior performance compared to other meta-heuristic algorithms across various weather conditions.
  • Achieved an average tracking efficiency of 99.99% under tested conditions.
  • Attained an average tracking time of 0.3886 seconds, indicating significant improvement in convergence speed.

Conclusions:

  • The proposed IEHO algorithm effectively addresses the challenge of local optima in MPPT under partial shading.
  • IEHO significantly enhances the speed and accuracy of global maximum power point tracking in photovoltaic systems.
  • The algorithm offers a promising solution for improving the overall energy conversion efficiency of PV systems in dynamic environments.