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Minimum non detection zone for islanding detection using an optimal Artificial Neural Network algorithm based on PSO

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  • Samet, Haidar
  • Hashemi, Farid
  • Ghanbari, Teymoor

Abstract

Islanding is one of the most important concerns of the grid connected distributed resources due to personnel and equipment safety. Many approaches have been proposed for islanding detection, which can be categorized into passive and active schemes. The main concern of the passive schemes is related to their large Non Detection Zone (NDZ), while the main problem of the active methods is related to their negative impact on power quality. This paper propose an efficient and intelligent islanding detection algorithm using combination of an optimal Artificial Neural Network (ANN) based on Particle Swarm Optimization (PSO) with a simple active method. The intelligent islanding detection method based on ANN, may have mal-detection in the case of change in the power network structure. In the proposed scheme, ANN is adapted with change in power network structure to reduce NDZ. Optimal parameters of the ANN such as weight coefficients and biases are derived using the PSO in order to minimize the technique NDZ. Also the performance of the various structures of ANN such as Multilayer Perceptron (MLP), Radial Basis Function (RBF) and Probabilistic Neural Network (PNN) in combination with PSO is compared for islanding detection purpose. The proposed method is simulated and tested in various operation conditions such as islanding conditions, motor starting, capacitor bank switching and nonlinear load switching. The test results showed that it correctly detects the islanding operation and does not mal-operate in the other situations and has a small NDZ.

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  • 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.
  • Handle: RePEc:eee:rensus:v:52:y:2015:i:c:p:1-18
    DOI: 10.1016/j.rser.2015.07.080
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    References listed on IDEAS

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    1. Chowdhury, S.P. & Chowdhury, S. & Crossley, P.A. & Gaunt, C.T., 2009. "RETRACTED: UK scenario of islanded operation of active distribution networks with renewable distributed generators," Renewable Energy, Elsevier, vol. 34(12), pages 2585-2591.
    2. Li, Canbing & Cao, Chi & Cao, Yijia & Kuang, Yonghong & Zeng, Long & Fang, Baling, 2014. "A review of islanding detection methods for microgrid," Renewable and Sustainable Energy Reviews, Elsevier, vol. 35(C), pages 211-220.
    3. Kalogirou, Soteris A., 2001. "Artificial neural networks in renewable energy systems applications: a review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 5(4), pages 373-401, December.
    4. Lidula, N.W.A. & Rajapakse, A.D., 2011. "Microgrids research: A review of experimental microgrids and test systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 15(1), pages 186-202, January.
    5. 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.
    6. Indu Rani, B. & Srikanth, M. & Saravana Ilango, G. & Nagamani, C., 2013. "An active islanding detection technique for current controlled inverter," Renewable Energy, Elsevier, vol. 51(C), pages 189-196.
    7. Karegar, H. Kazemi & Sobhani, B., 2012. "Wavelet transform method for islanding detection of wind turbines," Renewable Energy, Elsevier, vol. 38(1), pages 94-106.
    8. Ku Ahmad, Ku Nurul Edhura & Selvaraj, Jeyraj & Rahim, Nasrudin Abd, 2013. "A review of the islanding detection methods in grid-connected PV inverters," Renewable and Sustainable Energy Reviews, Elsevier, vol. 21(C), pages 756-766.
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    Cited by:

    1. Sahebi, Ali & Samet, Haidar & Ghanbari, Teymoor, 2017. "Evaluation of power transformer inrush currents and internal faults discrimination methods in presence of fault current limiter," Renewable and Sustainable Energy Reviews, Elsevier, vol. 68(P1), pages 102-112.
    2. 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.
    3. Xinxin Zheng & Rui Zhang & Xi Chen & Nong Sun, 2018. "Improved Three-Phase AFD Islanding Detection Based on Digital Control and Non-Detection Zone Elimination," Energies, MDPI, vol. 11(9), pages 1-15, September.
    4. Ibrahim, Thamir k. & Mohammed, Mohammed Kamil & Awad, Omar I. & Rahman, M.M. & Najafi, G. & Basrawi, Firdaus & Abd Alla, Ahmed N. & Mamat, Rizalman, 2017. "The optimum performance of the combined cycle power plant: A comprehensive review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 79(C), pages 459-474.
    5. Masoud Ahmadipour & Hashim Hizam & Mohammad Lutfi Othman & Mohd Amran Mohd Radzi & Nikta Chireh, 2019. "A Fast Fault Identification in a Grid-Connected Photovoltaic System Using Wavelet Multi-Resolution Singular Spectrum Entropy and Support Vector Machine," Energies, MDPI, vol. 12(13), pages 1-18, June.
    6. Balamurugan, M. & Sahoo, Sarat Kumar & Sukchai, Sukruedee, 2017. "Application of soft computing methods for grid connected PV system: A technological and status review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 75(C), pages 1493-1508.
    7. Samet, Haidar, 2016. "Evaluation of digital metering methods used in protection and reactive power compensation of micro-grids," Renewable and Sustainable Energy Reviews, Elsevier, vol. 62(C), pages 260-279.
    8. Khan, Mohammed Ali & Haque, Ahteshamul & Kurukuru, V.S. Bharath & Saad, Mekhilef, 2022. "Islanding detection techniques for grid-connected photovoltaic systems-A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 154(C).
    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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