Computational Intelligence based techniques for islanding detection of distributed generation in distribution network: A review

被引:74
|
作者
Laghari, J. A. [1 ,4 ]
Mokhlis, H. [1 ]
Karimi, M. [1 ]
Bakar, A. H. A. [2 ]
Mohamad, Hasmaini [3 ]
机构
[1] Univ Malaya, Fac Engn, Dept Elect Engn, Kuala Lumpur 50603, Malaysia
[2] Univ Malaya, Power Energy Dedicated Adv Ctr UMPEDAC, Wisma R&D UM, Kuala Lumpur 59990, Malaysia
[3] Univ Technol MARA UiTM, Fac Elect Engn, Shah Alam 40450, Selangor, Malaysia
[4] Quaid E Awam Univ Engn Sci & Technol, Dept Elect Engn, Nawabshah 67480, Sindh, Pakistan
关键词
Islanding detection; Artificial neural network; Fuzzy logic control; Adaptive Neuro fuzzy inference system; Decision tree classifier; PATTERN-RECOGNITION APPROACH; LOAD SHEDDING SCHEME; WAVELET TRANSFORM; TRANSIENT SIGNALS; MAINS DETECTION; UNITED-STATES; POWER; INVERTER; SYSTEM; PERFORMANCE;
D O I
10.1016/j.enconman.2014.08.024
中图分类号
O414.1 [热力学];
学科分类号
摘要
Accurate and fast islanding detection of distributed generation is highly important for its successful operation in distribution networks. Up to now, various islanding detection technique based on communication, passive, active and hybrid methods have been proposed. However, each technique suffers from certain demerits that cause inaccuracies in islanding detection. Computational intelligence based techniques, due to their robustness and flexibility in dealing with complex nonlinear systems, is an option that might solve this problem. This paper aims to provide a comprehensive review of computational intelligence based techniques applied for islanding detection of distributed generation. Moreover, the paper compares the accuracies of computational intelligence based techniques over existing techniques to provide a handful of information for industries and utility researchers to determine the best method for their respective system. (C) 2014 Elsevier Ltd. All rights reserved.
引用
收藏
页码:139 / 152
页数:14
相关论文
共 50 条
  • [1] Application of signal processing techniques for islanding detection of distributed generation in distribution network: A review
    Raza, Safdar
    Mokhlis, Hazlie
    Arof, Hamzah
    Laghari, J. A.
    Wang, Li
    [J]. ENERGY CONVERSION AND MANAGEMENT, 2015, 96 : 613 - 624
  • [2] A review of islanding detection techniques for renewable distributed generation systems
    Khamis, Aziah
    Shareef, Hussain
    Bizkevelci, Erdal
    Khatib, Tamer
    [J]. RENEWABLE & SUSTAINABLE ENERGY REVIEWS, 2013, 28 : 483 - 493
  • [3] Islanding Detection Techniques for Grid Integrated Distributed Generation -A Review
    Reddy, Ch. Rami
    Reddy, K. Harinadha
    [J]. INTERNATIONAL JOURNAL OF RENEWABLE ENERGY RESEARCH, 2019, 9 (02): : 960 - 977
  • [4] Probabilistic Neural Network Based Islanding Detection in Distributed Generation
    Samantaray, S. R.
    Babu, B. Chitti
    Dash, P. K.
    [J]. ELECTRIC POWER COMPONENTS AND SYSTEMS, 2011, 39 (03) : 191 - 203
  • [5] Evaluation of Islanding Detection Techniques for Inverter-Based Distributed Generation
    Faqhruldin, Omar N.
    El-Saadany, E. F.
    Zeineldin, H. H.
    [J]. 2012 IEEE POWER AND ENERGY SOCIETY GENERAL MEETING, 2012,
  • [6] Review of islanding detection methods for distributed generation
    Mahat, Pukar
    Chen, Zhe
    Bak-Jensen, Birgitte
    [J]. 2008 THIRD INTERNATIONAL CONFERENCE ON ELECTRIC UTILITY DEREGULATION AND RESTRUCTURING AND POWER TECHNOLOGIES, VOLS 1-6, 2008, : 2743 - 2748
  • [7] Taxonomy of Islanding detection techniques for distributed generation in microgrid
    Mishra, Manohar
    Chandak, Sheetal
    Rout, Pravat Kumar
    [J]. RENEWABLE ENERGY FOCUS, 2019, 31 : 9 - 30
  • [8] Radial Basis Neural Network Based Islanding Detection in Distributed Generation
    Hagh, M. Tarafdar
    Ghadimi, N.
    [J]. INTERNATIONAL JOURNAL OF ENGINEERING, 2014, 27 (07): : 1061 - 1069
  • [9] Review on Islanding Operation of Distribution System with Distributed Generation
    Mahat, Pukar
    Chen, Zhe
    Bak-Jensen, Birgitte
    [J]. 2011 IEEE POWER AND ENERGY SOCIETY GENERAL MEETING, 2011,
  • [10] Islanding Detection in Distributed Generation using Unsupervised Learning Techniques
    Biaz, B. M.
    Ferreira, V. H.
    Fortes, M. Z.
    Lopes, T. T.
    Lima, G. B. A.
    [J]. IEEE LATIN AMERICA TRANSACTIONS, 2018, 16 (01) : 118 - 125