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An Anti-Islanding Protection Technique Using a Wavelet Packet Transform and a Probabilistic Neural Network

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  • Masoud Ahmadipour

    (Department of Electrical and Electronic Engineering, Universiti Putra Malaysia, Serdang 43400, Selangor, Malaysia
    Center of Advanced Power and Energy Research (CAPER), Universiti Putra Malaysia, Serdang 43400, Selangor, Malaysia)

  • Hashim Hizam

    (Department of Electrical and Electronic Engineering, Universiti Putra Malaysia, Serdang 43400, Selangor, Malaysia
    Center of Advanced Power and Energy Research (CAPER), Universiti Putra Malaysia, Serdang 43400, Selangor, Malaysia)

  • Mohammad Lutfi Othman

    (Department of Electrical and Electronic Engineering, Universiti Putra Malaysia, Serdang 43400, Selangor, Malaysia
    Center of Advanced Power and Energy Research (CAPER), Universiti Putra Malaysia, Serdang 43400, Selangor, Malaysia)

  • Mohd Amran Mohd Radzi

    (Department of Electrical and Electronic Engineering, Universiti Putra Malaysia, Serdang 43400, Selangor, Malaysia
    Center of Advanced Power and Energy Research (CAPER), Universiti Putra Malaysia, Serdang 43400, Selangor, Malaysia)

Abstract

This paper proposes a new islanding detection technique based on the combination of a wavelet packet transform (WPT) and a probabilistic neural network (PNN) for grid-tied photovoltaic systems. The point of common coupling (PCC) voltage is measured and processed by the WPT to find the normalized Shannon entropy (NSE) and the normalized logarithmic energy entropy (NLEE). Subsequently, the yield feature vectors are fed to the PNN classifier to classify the disturbances. The PNN is trained with different spread factors to obtain better classification accuracy. For the best performance of the proposed method, the precise analysis is done for the selection of the type of input data for the PNN, the type of mother wavelet, and the required transform level which is based on the accuracy, simplicity, specificity, speed, and cost parameters. The results show that, by using normalized Shannon entropy and the normalized logarithmic energy entropy, not only it offers simplicity, specificity and reduced costs, it also has better accuracy compared to other smart and passive methods. Based on the results, the proposed islanding detection technique is highly accurate and does not mal-operate during islanding and non-islanding events.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:10:p:2701-:d:174798
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    References listed on IDEAS

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    1. Karimi, M. & Mokhlis, H. & Naidu, K. & Uddin, S. & Bakar, A.H.A., 2016. "Photovoltaic penetration issues and impacts in distribution network – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 53(C), pages 594-605.
    2. 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.
    3. Ponta, Linda & Carbone, Anna, 2018. "Information measure for financial time series: Quantifying short-term market heterogeneity," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 510(C), pages 132-144.
    4. Ponta, Linda & Raberto, Marco & Teglio, Andrea & Cincotti, Silvano, 2018. "An Agent-based Stock-flow Consistent Model of the Sustainable Transition in the Energy Sector," Ecological Economics, Elsevier, vol. 145(C), pages 274-300.
    5. Karegar, H. Kazemi & Sobhani, B., 2012. "Wavelet transform method for islanding detection of wind turbines," Renewable Energy, Elsevier, vol. 38(1), pages 94-106.
    6. Kuei-Hsiang Chao & Min-Sen Yang & Chin-Pao Hung, 2013. "Islanding Detection Method of a Photovoltaic Power Generation System Based on a CMAC Neural Network," Energies, MDPI, vol. 6(8), pages 1-18, August.
    7. Kong, Xiangrui & Xu, Xiaoyuan & Yan, Zheng & Chen, Sijie & Yang, Huoming & Han, Dong, 2018. "Deep learning hybrid method for islanding detection in distributed generation," Applied Energy, Elsevier, vol. 210(C), pages 776-785.
    8. Linda Ponta & Anna Carbone, 2017. "Information measure for financial time series: quantifying short-term market heterogeneity," Papers 1710.07331, arXiv.org, revised Feb 2018.
    9. Khamis, Aziah & Shareef, Hussain & Bizkevelci, Erdal & Khatib, Tamer, 2013. "A review of islanding detection techniques for renewable distributed generation systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 28(C), pages 483-493.
    10. Guo, Xiaoqiang & Xu, David & Wu, Bin, 2014. "Overview of anti-islanding US patents for grid-connected inverters," Renewable and Sustainable Energy Reviews, Elsevier, vol. 40(C), pages 311-317.
    11. 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.
    12. 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.
    13. 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.
    14. Allegra De Filippo & Michele Lombardi & Michela Milano, 2017. "User-Aware Electricity Price Optimization for the Competitive Market," Energies, MDPI, vol. 10(9), pages 1-23, September.
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    Cited by:

    1. 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.
    2. Karthikeyan Subramanian & Ashok Kumar Loganathan, 2020. "Islanding Detection Using a Micro-Synchrophasor for Distribution Systems with Distributed Generation," Energies, MDPI, vol. 13(19), pages 1-31, October.
    3. 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.
    4. Qiang Guo & Jing Wu & Haibin Jin & Cheng Peng, 2018. "An Innovative Calibration Scheme for Interharmonic Analyzers in Power Systems under Asynchronous Sampling," Energies, MDPI, vol. 12(1), pages 1-10, December.
    5. 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.
    6. 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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