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Classification of Many Abnormal Events in Radial Distribution Feeders Using the Complex Morlet Wavelet and Decision Trees

Author

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  • Mishari Metab Almalki

    (Department of Electrical and Computer Engineering, Southern Illinois University, 1230 Lincoln Dr., Carbondale 62901, IL, USA
    Department of Electrical Engineering, Al Baha University, Al Baha 65527, Saudi Arabia)

  • Constantine J. Hatziadoniu

    (Department of Electrical and Computer Engineering, Southern Illinois University, 1230 Lincoln Dr., Carbondale 62901, IL, USA)

Abstract

Monitoring of abnormal events in a distribution feeder by using a single technique is a challenging task. A number of abnormal events can cause unsafe operation, including a high impedance fault (HIF), a partial breakdown to a cable insulation, and a circuit breaker (CB) malfunction due to capacitor bank de-energization. These abnormal events are not detectable by conventional protection schemes. In this paper, a new technique to identify distribution feeder events is proposed based on the complex Morlet wavelet (CMW) and on a decision tree (DT) classifier. First, the event is detected using CMW. Subsequently, a DT using event signatures classifies the event as normal operation, continuous and non-continuous arcing events (C.A.E. and N.C.A.E.). Additional information from the supervisory control and data acquisition (SCADA) can be used to precisely identify the event. The proposed method is meticulously tested on the IEEE 13- and IEEE 34-bus systems and has shown to correctly classify those events. Furthermore, the proposed method is capable of detecting very high impedance incipient faults (IFs) and CB restrikes at the substation level with relatively short detection time. The proposed method uses only current measurements at a low sampling rate of 1440 Hz yielding an improvement of existing methods that require much higher sampling rates.

Suggested Citation

  • Mishari Metab Almalki & Constantine J. Hatziadoniu, 2018. "Classification of Many Abnormal Events in Radial Distribution Feeders Using the Complex Morlet Wavelet and Decision Trees," Energies, MDPI, vol. 11(3), pages 1-16, March.
  • Handle: RePEc:gam:jeners:v:11:y:2018:i:3:p:546-:d:134580
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    Cited by:

    1. Stéfano Frizzo Stefenon & Roberto Zanetti Freire & Leandro dos Santos Coelho & Luiz Henrique Meyer & Rafael Bartnik Grebogi & William Gouvêa Buratto & Ademir Nied, 2020. "Electrical Insulator Fault Forecasting Based on a Wavelet Neuro-Fuzzy System," Energies, MDPI, vol. 13(2), pages 1-19, January.
    2. Francinei L. Vieira & Pedro H. M. Santos & José M. Carvalho Filho & Roberto C. Leborgne & Marino P. Leite, 2019. "A Voltage-Based Approach for Series High Impedance Fault Detection and Location in Distribution Systems Using Smart Meters," Energies, MDPI, vol. 12(15), pages 1-16, August.
    3. Veerapandiyan Veerasamy & Noor Izzri Abdul Wahab & Rajeswari Ramachandran & Muhammad Mansoor & Mariammal Thirumeni & Mohammad Lutfi Othman, 2018. "High Impedance Fault Detection in Medium Voltage Distribution Network Using Discrete Wavelet Transform and Adaptive Neuro-Fuzzy Inference System," Energies, MDPI, vol. 11(12), pages 1-24, November.

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