Maintaining Electric Power Quality Using Integrated S-Transform with Xenogeneic Composition Neural Network

  • Authors

    • Haider Ahmed Mohmmed
    • Layth Mohammed Abd Ali
    • Othman M. Hussein Anssari
    https://doi.org/10.14419/ijet.v7i3.20.22936
  • Electric power quality, frequency, voltage and waveform, S-transform, particle multi-perceptron neural network, xenogeneic composition neural network

  • Abstract

    Electric power quality is one of a most important factor in industrial applications because it consists of collection of frequency, voltage and waveform information that used to enhance the electric power distribution. But the quality of power system has been affected by different factors such as swell, impulsive transients, momentary interruptions, swag, harmonics, notch and spike which leads to reducing the quality of power. So, the power quality is enhanced with the help of optimized machine learning techniques. Initially, the S-transform has been combined by particle multi-perceptron neural network (PMPNN) which examines the high and low-frequency components for analyzing the disturbance parameter and the further process is improved by integrating the S-transform with xenogeneic composition neural network (XGCNN) which train the power system features for eliminating the disturbance factors with effective manner. Finally, the simulation results are discussed for examining the power quality distribution factor with effective manner.

     

  • References

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  • How to Cite

    Ahmed Mohmmed, H., Mohammed Abd Ali, L., & M. Hussein Anssari, O. (2018). Maintaining Electric Power Quality Using Integrated S-Transform with Xenogeneic Composition Neural Network. International Journal of Engineering & Technology, 7(3.20), 538-543. https://doi.org/10.14419/ijet.v7i3.20.22936

    Received date: 2018-12-02

    Accepted date: 2018-12-02