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Training of the Naïve Bayes Classifier for the Detection of the Power Quality Events (Voltage Dip, Voltage Swell and Voltage Interruption)

Author

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  • Oluwaseun Elijah Adegbite

    (Ladoke Akintola University of Technology, Nigeria.)

  • M. O. Okelola

    (Ladoke Akintola University of Technology, Nigeria.)

Abstract

The effect of the Power Quality events can be devastating if not properly managed. To manage such PQ events effective detection and classification techniques must be developed. There are various mathematical models that can be used in the detection and classification of the events which could vary from Dip, Swell, Interruption, and Harmonic distortion. The paper is based on the classification of Voltage Dip, Voltage Swell and Voltage Interruption using the STFT as the method of the detection of the triggering point and using such synthetic signal to train the Naïve Bayes classifier to develop a classifier that is capable of classifying waveform signals that has such disturbances in them.

Suggested Citation

  • Oluwaseun Elijah Adegbite & M. O. Okelola, 2020. "Training of the Naïve Bayes Classifier for the Detection of the Power Quality Events (Voltage Dip, Voltage Swell and Voltage Interruption)," European Journal of Electrical Engineering and Computer Science, European Open Science, vol. 4(4), July.
    • Handle: RePEc:epw:ejece0:v:4:y:2020:i:4:id:19222
    DOI: 10.24018/ejece.2020.4.4.222
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