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Application of decision tree and discrete wavelet transform for an optimized intelligent-based islanding detection method in distributed systems with distributed generations

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  • Heidari, Mehrdad
  • Seifossadat, Ghodratollah
  • Razaz, Morteza

Abstract

In this paper, a method for islanding detection based on analysis of transient state signals is provided. Decision tree (DT) is trained for classifying the transient events. The required features for classifying are extracted through discrete wavelet transform (DWT) of signals. The proposed method is then simulated on a medium voltage distribution system of CIGRE with two kinds of distributed generations (DGs) using DIgSILENT, MATLAB and WEKA softwares. By analysis performed on type of input signal, type of mother wavelet and required transform level, among 162 relay designs, an optimum relay is selected for distributed generations (DGs) based on accuracy, speed, simplicity and cost parameters. By evaluation, it is determined that using only one input (voltage) signal not only improves speed and simplicity and reduces costs, also makes accuracy of the proposed relay better than other intelligent and passive methods. The final selected relay for each DG is V-db4-D3 which has accuracy equal 98%.

Suggested Citation

  • Heidari, Mehrdad & Seifossadat, Ghodratollah & Razaz, Morteza, 2013. "Application of decision tree and discrete wavelet transform for an optimized intelligent-based islanding detection method in distributed systems with distributed generations," Renewable and Sustainable Energy Reviews, Elsevier, vol. 27(C), pages 525-532.
    • Handle: RePEc:eee:rensus:v:27:y:2013:i:c:p:525-532
    DOI: 10.1016/j.rser.2013.06.047
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    Citations

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    Cited by:

    1. Min-Sung Kim & Raza Haider & Gyu-Jung Cho & Chul-Hwan Kim & Chung-Yuen Won & Jong-Seo Chai, 2019. "Comprehensive Review of Islanding Detection Methods for Distributed Generation Systems," Energies, MDPI, vol. 12(5), pages 1-21, March.
    2. Singh, Bindeshwar & Mukherjee, V. & Tiwari, Prabhakar, 2015. "A survey on impact assessment of DG and FACTS controllers in power systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 42(C), pages 846-882.
    3. Bayrak, Gökay & Kabalci, Ersan, 2016. "Implementation of a new remote islanding detection method for wind–solar hybrid power plants," Renewable and Sustainable Energy Reviews, Elsevier, vol. 58(C), pages 1-15.
    4. S. Ananda Kumar & M. S. P. Subathra & Nallapaneni Manoj Kumar & Maria Malvoni & N. J. Sairamya & S. Thomas George & Easter S. Suviseshamuthu & Shauhrat S. Chopra, 2020. "A Novel Islanding Detection Technique for a Resilient Photovoltaic-Based Distributed Power Generation System Using a Tunable-Q Wavelet Transform and an Artificial Neural Network," Energies, MDPI, vol. 13(16), pages 1-22, August.
    5. Samet, Haidar & Hashemi, Farid & Ghanbari, Teymoor, 2015. "Minimum non detection zone for islanding detection using an optimal Artificial Neural Network algorithm based on PSO," Renewable and Sustainable Energy Reviews, Elsevier, vol. 52(C), pages 1-18.
    6. Vyas, Shashank & Kumar, Rajesh & Kavasseri, Rajesh, 2017. "Data analytics and computational methods for anti-islanding of renewable energy based Distributed Generators in power grids," Renewable and Sustainable Energy Reviews, Elsevier, vol. 69(C), pages 493-502.
    7. Muttaqi, Kashem M. & Aghaei, Jamshid & Ganapathy, Velappa & Nezhad, Ali Esmaeel, 2015. "Technical challenges for electric power industries with implementation of distribution system automation in smart grids," Renewable and Sustainable Energy Reviews, Elsevier, vol. 46(C), pages 129-142.
    8. Ahmadipour, Masoud & Hizam, Hashim & Othman, Mohammad Lutfi & Radzi, Mohd Amran Mohd & Murthy, Avinash Srikanta, 2018. "Islanding detection technique using Slantlet Transform and Ridgelet Probabilistic Neural Network in grid-connected photovoltaic system," Applied Energy, Elsevier, vol. 231(C), pages 645-659.
    9. Masoud Ahmadipour & Hashim Hizam & Mohammad Lutfi Othman & Mohd Amran Mohd Radzi, 2018. "An Anti-Islanding Protection Technique Using a Wavelet Packet Transform and a Probabilistic Neural Network," Energies, MDPI, vol. 11(10), pages 1-31, October.

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