Fast discrete s-transform and extreme learning machine based approach to islanding detection in grid-connected distributed generation

This paper presents an approach for islanding detection in distributed generation systems (DGs) using fast discrete s-transform (FDST) algorithm and bidirectional extreme learning machine (BELM) classifier. The system undertaken for this study comprises of two different kinds of DGs such as hydro turbine governor system and wind turbine generator. The analysis starts with extracting the non-stationary negative sequence voltage and current signals at DG end and then the instantaneous amplitude and frequency information are extracted through FDST algorithm. From this information, different distinguishing features are computed such as energy and standard deviation of amplitude to track the islanded event from different non-islanded events. The obtained features are examined through an extreme learning machine classifier to discriminate islanding and non-islanding events, under various operating conditions of distribution system. The accuracy of the proposed method is compared with other recently published techniques by various researchers to justify its improved performance for islanding detection.

[1]  K. S. Banerjee.  Generalized Inverse of Matrices and Its Applications , 1973 .

[2]  B. Fox,et al.  Loss of mains detection for embedded generation by system impedance monitoring , 1997 .

[3]  Ajeet Rohatgi,et al.  Determining the relative effectiveness of islanding detection methods using phase criteria and nondetection zones , 2000 .

[4]  S. K. Salman,et al.  New loss of mains detection algorithm for embedded generation using rate of change of voltage and changes in power factors , 2001 .

[5]  S. J. Huang,et al.  A Detection Algorithm for Islanding-Prevention of Dispersed Consumer-Owned Storage and Generating Units , 2001, IEEE Power Engineering Review.

[6]  Kwang-Ho Kim,et al.  An islanding detection method for distributed generations using voltage unbalance and total harmonic distortion of current , 2004 .

[7]  Wilsun Xu,et al.  Comparative analysis between ROCOF and vector surge relays for distributed generation applications , 2005, IEEE Transactions on Power Delivery.

[8]  Peter Crossley,et al.  Islanding detection for distributed generation , 2005 .

[9]  Narasimhan Sundararajan,et al.  A Fast and Accurate Online Sequential Learning Algorithm for Feedforward Networks , 2006, IEEE Transactions on Neural Networks.

[10]  M. Liserre,et al.  Overview of Anti-Islanding Algorithms for PV Systems. Part I: Passive Methods , 2006, 2006 12th International Power Electronics and Motion Control Conference.

[11]  Chee Kheong Siew,et al.  Universal Approximation using Incremental Constructive Feedforward Networks with Random Hidden Nodes , 2006, IEEE Transactions on Neural Networks.

[12]  Chee Kheong Siew,et al.  Extreme learning machine: Theory and applications , 2006, Neurocomputing.

[13]  Guang-Bin Huang,et al.  Convex incremental extreme learning machine , 2007, Neurocomputing.

[14]  Ehab F. El-Saadany,et al.  Islanding detection of grid connected distributed generators using TLS-ESPRIT , 2007 .

[15]  G. Joos,et al.  Intelligent-Based Approach to Islanding Detection in Distributed Generation , 2007, IEEE Transactions on Power Delivery.

[16]  Lei Chen,et al.  Enhanced random search based incremental extreme learning machine , 2008, Neurocomputing.

[17]  Stefania Conti,et al.  Analysis of distribution network protection issues in presence of dispersed generation , 2009 .

[18]  S.R. Samantaray,et al.  A new approach to islanding detection in distributed generations , 2009, 2009 International Conference on Power Systems.

[19]  Zhengming Zhao,et al.  Grid-connected photovoltaic power systems: Technical and potential problems—A review , 2010 .

[20]  Amaury Lendasse,et al.  OP-ELM: Optimally Pruned Extreme Learning Machine , 2010, IEEE Transactions on Neural Networks.

[21]  Ahmed Osman,et al.  Neuro-wavelet based islanding detection technique , 2010, 2010 IEEE Electrical Power & Energy Conference.

[22]  Nand Kishor,et al.  S-transform based islanding detection in grid-connected distributed generation based power system , 2010, 2010 IEEE International Energy Conference.

[23]  A. Samui,et al.  S-transform based cumulative sum detector (CUSUM ) for islanding detection in Distributed Generations , 2010, 2010 Joint International Conference on Power Electronics, Drives and Energy Systems & 2010 Power India.

[24]  Ehab F. El-Saadany,et al.  A robust wavelet-ANN based technique for islanding detection , 2011, 2011 IEEE Power and Energy Society General Meeting.

[25]  Athula Rajapakse,et al.  A pattern recognition approach for detecting power islands using transient signals — Part I: Design and implementation , 2011, 2011 IEEE Power and Energy Society General Meeting.

[26]  Nand Kishor,et al.  Disturbance detection in grid-connected distributed generation system using wavelet and S-transform , 2011 .

[27]  Chung-Yuen Won,et al.  An Islanding Detection Method for a Grid-Connected System Based on the Goertzel Algorithm , 2011, IEEE Transactions on Power Electronics.

[28]  Mohsen Karimi,et al.  Intelligent islanding detection of a synchronous distributed generation using governor signal cluste , 2011 .

[29]  Il-Song Kim,et al.  Islanding Detection Technique using Grid-Harmonic Parameters in the Photovoltaic System , 2012 .

[30]  Hongming Zhou,et al.  Extreme Learning Machine for Regression and Multiclass Classification , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[31]  João P. S. Catalão,et al.  Islanding detection in a distributed generation based hybrid system using intelligent pattern recognition techniques , 2012, 2012 3rd IEEE PES Innovative Smart Grid Technologies Europe (ISGT Europe).

[32]  Panos M. Pardalos,et al.  A mixed integer programming approach for optimal power grid intentional islanding , 2012, Energy Systems.

[33]  Yaonan Wang,et al.  Bidirectional Extreme Learning Machine for Regression Problem and Its Learning Effectiveness , 2012, IEEE Transactions on Neural Networks and Learning Systems.

[34]  K. R. Krishnanand,et al.  A New Real-Time Fast Discrete S-Transform for Cross-Differential Protection of Shunt-Compensated Power Systems , 2013, IEEE Transactions on Power Delivery.

[35]  Tianshu Bi,et al.  Advanced islanding detection utilized in distribution systems with DFIG , 2014 .

[36]  Saeed Afsharnia,et al.  A new passive islanding detection method and its performance evaluation for multi-DG systems , 2014 .

[37]  J. A. Laghari,et al.  Computational Intelligence based techniques for islanding detection of distributed generation in distribution network: A review , 2014 .

[38]  Walid G. Morsi,et al.  A New Approach Based on Wavelet Design and Machine Learning for Islanding Detection of Distributed Generation , 2014, IEEE Transactions on Smart Grid.

[39]  S. Patel,et al.  A novel islanding detection technique based on wavelet packet transform , 2015, 2015 IEEE Power, Communication and Information Technology Conference (PCITC).

[40]  Kai Sun,et al.  A decision tree based approach for microgrid islanding detection , 2015, 2015 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT).

[41]  Li Wang,et al.  Application of signal processing techniques for islanding detection of distributed generation in distribution network : A review , 2015 .

[42]  Joao P. S. Catalao,et al.  Comparative Study of Advanced Signal Processing Techniques for Islanding Detection in a Hybrid Distributed Generation System , 2015, IEEE Transactions on Sustainable Energy.

[43]  Glauco N. Taranto,et al.  Pilot field test of intentional islanding in distribution network , 2015 .

[44]  Suttichai Premrudeepreechacharn,et al.  Over/Undervoltage and Undervoltage Shift of Hybrid Islanding Detection Method of Distributed Generation , 2015, TheScientificWorldJournal.

[45]  Xing Zhang,et al.  Passive-Islanding Detection Method Using the Wavelet Packet Transform in Grid-Connected Photovoltaic Systems , 2016, IEEE Transactions on Power Electronics.

[46]  Manohar Mishra,et al.  Time-Frequency Analysis based Approach to Islanding Detection in Micro-grid System , 2016 .

[47]  Mohammad Mohammadi,et al.  Islanding detection approach with negligible non‐detection zone based on feature extraction discrete wavelet transform and artificial neural network , 2016 .

[48]  Jianhui Wang,et al.  Large-scale stochastic power grid islanding operations by line switching and controlled load shedding , 2017 .

[49]  Mrutyunjaya Sahani,et al.  An islanding detection algorithm for distributed generation based on Hilbert–Huang transform and extreme learning machine , 2017 .

[50]  Susmita Kar,et al.  A comprehensive protection scheme for micro-grid using fuzzy rule base approach , 2017 .