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Support vector machine optimization via an improved elephant herding algorithm for motor energy efficiency rating.

Xinrui Ren1, Jianbo Yu2, Zhaomin Lv3

  • 1School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China.

Mathematical Biosciences and Engineering : MBE
|January 19, 2023
PubMed
Summary
This summary is machine-generated.

Accurate motor energy efficiency evaluation is crucial for energy savings. An improved elephant herding optimization (EHO) method with a support vector machine achieved over 98% accuracy in rating motor efficiency.

Keywords:
improved elephant herding optimizationmotor energy efficiency gradingsupport vector machine

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Area of Science:

  • Electrical Engineering
  • Computational Intelligence

Background:

  • Evaluating motor energy efficiency during off-conditions is vital for energy conservation and eliminating inefficient motors.
  • Existing methods may struggle with slow convergence and local optima, hindering accurate assessment.

Purpose of the Study:

  • To develop an accurate motor energy efficiency evaluation system.
  • To enhance the elephant herd optimization (EHO) algorithm for improved performance.

Main Methods:

  • A motor energy efficiency evaluation system with seven indexes and 10 grades was designed, incorporating power quality, motor, and load characteristics.
  • An improved EHO algorithm was developed using chaotic mapping, adversarial learning, group learning, and elite retention.
  • The improved EHO was combined with a support vector machine rating model.

Main Results:

  • The proposed system achieved an accuracy higher than 98% in evaluating motor energy efficiency.
  • The enhanced EHO demonstrated superior optimization performance compared to other intelligent algorithms on 12 benchmark functions.
  • Improvements in population initialization and local development enhanced the algorithm's diversity and global search capabilities.

Conclusions:

  • The enhanced EHO combined with a support vector machine provides a highly accurate method for motor energy efficiency evaluation.
  • The improved EHO algorithm offers better convergence speed, precision, and global search capability.
  • This approach facilitates energy-saving upgrades and the identification of inefficient motors.