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Power system events classification using genetic algorithm based feature weighting technique for support vector

Oyeniyi Akeem Alimi1, Khmaies Ouahada1, Adnan M Abu-Mahfouz1,2

  • 1Department of Electrical & Electronic Engineering Science, University of Johannesburg, Johannesburg 2006, South Africa.

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Summary
This summary is machine-generated.

This study introduces a hybrid genetic algorithm-support vector machine (GA-SVM) model for accurately detecting and classifying power system disturbances. The novel approach enhances stability by improving the classification rate of unwanted events.

Keywords:
ClassificationGenetic algorithmPower systemSupport vector machineSynchrophasors

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Area of Science:

  • Electrical Engineering
  • Computational Intelligence
  • Power Systems Analysis

Background:

  • Ensuring power system stability and security is a critical global challenge.
  • Injections and faults in generation and transmission systems are primary causes of instability.
  • Early detection and diagnosis of unwanted events are crucial for preventing blackouts.

Purpose of the Study:

  • To present a hybrid classification technique for detecting and classifying power system unwanted events.
  • To improve the accuracy and efficiency of power system disturbance analysis.
  • To develop a robust method for enhancing power system operational security.

Main Methods:

  • Feature reduction using Principal Component Analysis (PCA) on synchrophasor datasets.
  • Feature weighting and selection using the evolutionary Genetic Algorithm (GA).
  • Classification of events using Support Vector Machine (SVM) with linear and radial basis function kernels trained on GA-selected features.

Main Results:

  • The proposed GA-SVM model demonstrated high classification accuracy for power system unwanted events.
  • The GA effectively identified dominant features, significantly boosting SVM classification performance.
  • Experimental results showed an overall improvement in the classification rate compared to other models.

Conclusions:

  • The hybrid GA-SVM approach is a validated and effective method for power system disturbance detection and classification.
  • The application of GA as a feature weighting tool offers significant improvements in classification performance.
  • This technique contributes to enhanced power system stability, security, and operational efficiency.