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A Hybrid Fault Recognition Algorithm Using Stockwell Transform and Wigner Distribution Function for Power System Network with Solar Energy Penetration

Author

Listed:
  • Atul Kulshrestha

    (Department of Electrical Engineering, Suresh Gyan Vihar University, Jaipur 302017, India)

  • Om Prakash Mahela

    (Power System Planning Division, Rajasthan Rajya Vidhyut Prasaran Nigam Ltd., Jaipur 302005, India)

  • Mukesh Kumar Gupta

    (Department of Electrical Engineering, Suresh Gyan Vihar University, Jaipur 302017, India)

  • Neeraj Gupta

    (Department of Computer Science and Engineering, Oakland University, Rochester, MI 48084, USA)

  • Nilesh Patel

    (Department of Computer Science and Engineering, Oakland University, Rochester, MI 48084, USA)

  • Tomonobu Senjyu

    (Department of Electrical & Electronics Engineering, University of the Ryukyus, Senbaru, Okinawa 903-0213, Japan)

  • Mir Sayed Shah Danish

    (Department of Electrical & Electronics Engineering, University of the Ryukyus, Senbaru, Okinawa 903-0213, Japan)

  • Mahdi Khosravy

    (Media Integrated Communication Laboratory, Graduate School of Engineering, Osaka University, Osaka 565-0871, Japan)

Abstract

Penetration level of solar photovoltaic (PV) energy in the utility network is steadily increasing. This changes the fault level and causes protection problems. Furthermore, multi-tapped structure of distribution network deployed to integrate solar PV energy to the grid and supplying loads at the same time also raised the protection challenges. Hence, this manuscript is aimed at introducing an algorithm to identify and classify the faults incident on the network of utilities where penetration level of the solar PV energy is high. This fault recognition algorithm is implemented in four steps: (1) calculation of Stockwell transform-based fault index (STFI) (2) calculation of Wigner distribution function-based fault index (WDFI) (3) calculation of combined fault index (CFI) by multiplying STFI and WDFI (4) calculation of index for ground fault (IGF) used to recognize the involvement of ground in a fault event. The STFI has the merits that its performance is least affected by the noise associated with the current signals and it is effective in identification of the waveform distortions. The WDFI employs energy density of the current signals for estimation of the faults and takes care of the current magnitude. Hence, CFI has the merit that it considers the current magnitude as well as waveform distortion for recognition of the faults. The classification of faults is achieved using the number of faulty phases. An index for ground fault (IGF) based on currents of zero sequence is proposed to classify the two phase faults with and without the ground engagement. Investigated faults include phase to ground, two phases fault without involving ground, two phases fault involving ground and three phase fault. Fault recognition algorithm is tested for fault recognition with the presence of noise, various angles of fault incidence, different impedances involved during faulty event, hybrid lines consisting of overhead line (OHL) and underground cable (UGC) sections, and location of faults on all nodes of the test grid. Fault recognition algorithm is also tested to discriminate the transients due to switching operations of feeders, loads and capacitor banks from the faulty transients. Performance of the fault recognition algorithm is compared with the algorithms based on discrete wavelet transform (DWT), Stockwell transform (ST) and hybrid combination of alienation coefficient and Wigner distribution function (WDF). Effectiveness of the fault recognition algorithm is established using a detailed study on the IEEE-13 nodes test feeder modified to incorporate solar PV plant of capacity 1 MW in MATLAB/Simulink. Algorithm is also validated on practical utility grid of Rajasthan State of India.

Suggested Citation

  • Atul Kulshrestha & Om Prakash Mahela & Mukesh Kumar Gupta & Neeraj Gupta & Nilesh Patel & Tomonobu Senjyu & Mir Sayed Shah Danish & Mahdi Khosravy, 2020. "A Hybrid Fault Recognition Algorithm Using Stockwell Transform and Wigner Distribution Function for Power System Network with Solar Energy Penetration," Energies, MDPI, vol. 13(14), pages 1-25, July.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:14:p:3519-:d:382068
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    References listed on IDEAS

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    1. Mahela, Om Prakash & Shaik, Abdul Gafoor, 2017. "Power quality recognition in distribution system with solar energy penetration using S-transform and Fuzzy C-means clustering," Renewable Energy, Elsevier, vol. 106(C), pages 37-51.
    2. Sheesh Ram Ola & Amit Saraswat & Sunil Kumar Goyal & Virendra Sharma & Baseem Khan & Om Prakash Mahela & Hassan Haes Alhelou & Pierluigi Siano, 2020. "Alienation Coefficient and Wigner Distribution Function Based Protection Scheme for Hybrid Power System Network with Renewable Energy Penetration," Energies, MDPI, vol. 13(5), pages 1-25, March.
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    Cited by:

    1. Luigi Fortuna & Arturo Buscarino, 2022. "Sustainable Energy Systems," Energies, MDPI, vol. 15(23), pages 1-7, December.
    2. Yijin Li & Jianhua Lin & Geng Niu & Ming Wu & Xuteng Wei, 2021. "A Hilbert–Huang Transform-Based Adaptive Fault Detection and Classification Method for Microgrids," Energies, MDPI, vol. 14(16), pages 1-16, August.
    3. Gori Shankar Sharma & Om Prakash Mahela & Mohamed G. Hussien & Baseem Khan & Sanjeevikumar Padmanaban & Muhammed B. Shafik & Zakaria M. Salem Elbarbary, 2022. "Performance Evaluation of a MW-Size Grid-Connected Solar Photovoltaic Plant Considering the Impact of Tilt Angle," Sustainability, MDPI, vol. 14(3), pages 1-28, January.
    4. Rizwan Tariq & Ibrahim Alhamrouni & Ateeq Ur Rehman & Elsayed Tag Eldin & Muhammad Shafiq & Nivin A. Ghamry & Habib Hamam, 2022. "An Optimized Solution for Fault Detection and Location in Underground Cables Based on Traveling Waves," Energies, MDPI, vol. 15(17), pages 1-19, September.

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