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A New Approach to Optimize SVM for Insulator State Identification Based on Improved PSO Algorithm.

Lepeng Song1, Qin Liang1, Hui Chen1

  • 1School of Electrical Engineering, Chongqing University of Science and Technology, Chongqing 401331, China.

Sensors (Basel, Switzerland)
|January 8, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces an enhanced particle swarm optimization-support vector machine (PSO-SVM) for detecting defective high-voltage insulators from aerial images. The novel method achieves high accuracy, improving power system safety.

Keywords:
insulatorparticle swarm optimizationstate identificationsupport vector machine

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Area of Science:

  • Electrical Engineering
  • Computer Vision
  • Machine Learning

Background:

  • Power system safety relies on the integrity of high-voltage insulators.
  • Detecting insulator defects is crucial for preventing operational failures.
  • Aerial imagery offers a practical approach for large-scale insulator inspection.

Purpose of the Study:

  • To develop an advanced machine learning model for accurate insulator state identification.
  • To optimize the Support Vector Machine (SVM) classifier using an enhanced Particle Swarm Optimization (PSO) algorithm.
  • To improve the reliability and efficiency of high-voltage insulator defect detection.

Main Methods:

  • Image segmentation of insulators using seed region growth with morphological improvements.
  • Extraction of Histogram of Oriented Gradients (HOG) and Gray-Level Co-occurrence Matrix (GLCM) features.
  • Development of a PSO-optimized SVM (PSO-SVM) classifier for state identification.

Main Results:

  • The proposed PSO-SVM classifier achieved a recognition accuracy of 92.11%.
  • Performance metrics included a precision rate of 90%, recall rate of 94.74%, and F1-score of 92.31%.
  • The PSO-SVM method outperformed conventional algorithms like SVM, Random Forest, GWO-SVM, and CNN.

Conclusions:

  • The enhanced PSO-SVM algorithm demonstrates superior performance in identifying normal and defective high-voltage insulators.
  • This approach offers a robust and accurate solution for automated insulator state detection from aerial imagery.
  • The findings contribute to enhancing the safety and reliability of power system operations.