PQD classifier based on higher-order statistics and total harmonic distortion

Authors

  • Jesús-Manuel González-Bueno Author
  • José-Carlos Palomares-Salas Author
  • Juan-José González-de-la-Rosa Author
  • Olivia Florencias-Oliveros Author
  • José-María Sierra-Fernández Author
  • Manuel-Jesús Espinosa-Gavira Author
  • Agustín Agüera-Pérez Author

DOI:

https://doi.org/10.24084/repqj17.208

Keywords:

Higher-Order Statistics (HOS), Total Harmonic Distortion (THD), Feed-Forward Neural Network, Power Quality Disturbance (PQD)

Abstract

Higher-Order Statistics (HOS) have been frequently applied in Power Quality Disturbance (PQD) analysis as a reliable tool for event detection. This paper outlines a technique based on mean, variance and zero-lag third and fourth cumulants – skewness and kurtosis – along with the Total Harmonic Distortion (THD) index for PQD detection. These statistics are obtained in order to characterize a waveform by a feature vector. A two-layer feed-forward neural network is then used to classify inputs (feature vectors) into a set of PQD categories. The impact of frame duration and number of hidden neurons is analyzed. The network is trained, validated and tested with synthetically-generated PQD waveforms obtained from parameter-controlled equations. As a first approach, five PQD categories are considered: sag, swell, interruption, impulsive transient and oscillatory transient. A promising overall classification rate of 99.7 % is achieved which allows future analysis with more PQD categories and/or a noisy context.

Author Biographies

  • Jesús-Manuel González-Bueno

    University of Cádiz. Research Group PAIDI-TIC 168 – Computational Instrumentation and Industrial Electronics Dept. of Automation Engineering, Electronics, Architecture and Computer Networks Engineering School of Algeciras  Ramón Puyol Av. s/n, E11202 Algeciras (Spain) 

  • José-Carlos Palomares-Salas

    University of Cádiz. Research Group PAIDI-TIC 168 – Computational Instrumentation and Industrial Electronics Dept. of Automation Engineering, Electronics, Architecture and Computer Networks Engineering School of Algeciras  Ramón Puyol Av. s/n, E11202 Algeciras (Spain)

  • Juan-José González-de-la-Rosa

    University of Cádiz. Research Group PAIDI-TIC 168 – Computational Instrumentation and Industrial Electronics Dept. of Automation Engineering, Electronics, Architecture and Computer Networks Engineering School of Algeciras  Ramón Puyol Av. s/n, E11202 Algeciras (Spain) 

  • Olivia Florencias-Oliveros

    University of Cádiz. Research Group PAIDI-TIC 168 – Computational Instrumentation and Industrial Electronics Dept. of Automation Engineering, Electronics, Architecture and Computer Networks Engineering School of Algeciras  Ramón Puyol Av. s/n, E11202 Algeciras (Spain) 

  • José-María Sierra-Fernández

    University of Cádiz. Research Group PAIDI-TIC 168 – Computational Instrumentation and Industrial Electronics Dept. of Automation Engineering, Electronics, Architecture and Computer Networks Engineering School of Algeciras  Ramón Puyol Av. s/n, E11202 Algeciras (Spain) 

  • Manuel-Jesús Espinosa-Gavira

    University of Cádiz. Research Group PAIDI-TIC 168 – Computational Instrumentation and Industrial Electronics Dept. of Automation Engineering, Electronics, Architecture and Computer Networks Engineering School of Algeciras  Ramón Puyol Av. s/n, E11202 Algeciras (Spain) 

  • Agustín Agüera-Pérez

    University of Cádiz. Research Group PAIDI-TIC 168 – Computational Instrumentation and Industrial Electronics Dept. of Automation Engineering, Electronics, Architecture and Computer Networks Engineering School of Algeciras  Ramón Puyol Av. s/n, E11202 Algeciras (Spain) 

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Published

2024-01-08

Issue

Vol. 17 No. 1 (2019)

Section

Articles