Comparative Study of Advanced Signal Processing Techniques for Islanding Detection in a Hybrid Distributed Generation System

In this paper, islanding detection in a hybrid distributed generation (DG) system is analyzed by the use of hyperbolic S-transform (HST), time-time transform, and mathematical morphology methods. The merits of these methods are thoroughly compared against commonly adopted wavelet transform (WT) and S-transform (ST) techniques, as a new contribution to earlier studies. The hybrid DG system consists of photovoltaic and wind energy systems connected to the grid within the IEEE 30-bus system. Negative sequence component of the voltage signal is extracted at the point of common coupling and passed through the above-mentioned techniques. The efficacy of the proposed methods is also compared by an energy-based technique with proper threshold selection to accurately detect the islanding phenomena. Further, to augment the accuracy of the result, the classification is done using support vector machine (SVM) to distinguish islanding from other power quality (PQ) disturbances. The results demonstrate effective performance and feasibility of the proposed techniques for islanding detection under both noise-free and noisy environments, and also in the presence of harmonics.

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

[2]  A.K. Pradhan,et al.  A Cumulative Sum-Based Fault Detector for Power System Relaying Application , 2008, IEEE Transactions on Power Delivery.

[3]  C. Robert Pinnegar,et al.  Time-frequency and time-time filtering with the S-transform and TT-transform , 2005, Digit. Signal Process..

[4]  S. Kennedy,et al.  Instability criterion to eliminate the Non-Detection Zone of the Sandia Frequency Shift method , 2009, 2009 IEEE/PES Power Systems Conference and Exposition.

[5]  S. R. Mohanty,et al.  Classification of Power Quality Disturbances Due to Environmental Characteristics in Distributed Generation System , 2013, IEEE Transactions on Sustainable Energy.

[6]  N. W. A. Lidula,et al.  A Pattern-Recognition Approach for Detecting Power Islands Using Transient Signals—Part II: Performance Evaluation , 2012, IEEE Transactions on Power Delivery.

[7]  Shyh-Jier Huang,et al.  Enhancement of islanding-detection of distributed generation systems via wavelet transform-based approaches , 2008 .

[8]  Subhransu Ranjan Samantaray,et al.  Time-frequency transform-based islanding detection in distributed generation , 2011 .

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

[10]  Mehmet Uzunoglu,et al.  Modeling, control and simulation of an autonomous wind turbine/photovoltaic/fuel cell/ultra-capacitor hybrid power system , 2008 .

[11]  Peng Li,et al.  Detection of power quality disturbances in microgrid based on generalized morphological filter and backward difference , 2011, 2011 4th International Conference on Electric Utility Deregulation and Restructuring and Power Technologies (DRPT).

[12]  Nand Kishor,et al.  Islanding and Power Quality Disturbance Detection in Grid-Connected Hybrid Power System Using Wavelet and $S$-Transform , 2012, IEEE Transactions on Smart Grid.

[13]  Sukumar M. Brahma,et al.  Overview of mathematical morphology in power systems — A tutorial approach , 2009, 2009 IEEE Power & Energy Society General Meeting.

[14]  Jovitha Jerome,et al.  Pattern recognition of power signal disturbances using S Transform and TT Transform , 2010 .

[15]  Nikos D. Hatziargyriou,et al.  Operation of microgrids with demand side bidding and continuity of supply for critical loads , 2011 .

[16]  Magdy M. A. Salama,et al.  Impact of Load Frequency Dependence on the NDZ and Performance of the SFS Islanding Detection Method , 2011, IEEE Transactions on Industrial Electronics.

[17]  Malabika Basu,et al.  Development of EN50438 compliant wavelet-based islanding detection technique for three-phase static distributed generation systems , 2012 .

[18]  Nikos D. Hatziargyriou,et al.  Integrating distributed generation into electric power systems: A review of drivers, challenges and opportunities , 2007 .

[19]  Yong Kang,et al.  Experimental Investigation on Non Detection Zones of Active Frequency Drift Method for Anti-islanding , 2007, IECON 2007 - 33rd Annual Conference of the IEEE Industrial Electronics Society.

[20]  Bijaya Ketan Panigrahi,et al.  Classification of disturbances in hybrid DG system using modular PNN and SVM , 2013 .

[21]  Ehab F. El-Saadany,et al.  Impact of DG interface control on islanding detection and nondetection zones , 2006 .

[22]  Juan José Dañobeitia,et al.  On the TT-Transform and Its Diagonal Elements , 2008, IEEE Transactions on Signal Processing.

[23]  N. Ertugrul,et al.  Automatic Classification and Characterization of Power Quality Events , 2008, IEEE Transactions on Power Delivery.

[24]  A D Rajapakse,et al.  A Pattern Recognition Approach for Detecting Power Islands Using Transient Signals—Part I: Design and Implementation , 2010, IEEE Transactions on Power Delivery.

[25]  Bijaya K. Panigrahi,et al.  Non-stationary power signal processing for pattern recognition using HS-transform , 2009, Appl. Soft Comput..

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

[27]  Liu Hui-jin,et al.  Power quality disturbances detection and location using mathematical morphology and complex wavelet transformation , 2008, 2008 3rd IEEE Conference on Industrial Electronics and Applications.

[28]  Sukumar Brahma,et al.  Out-of-step blocking function in distance relay using mathematical morphology , 2012 .

[29]  G. Panda,et al.  Power Quality Analysis Using S-Transform , 2002, IEEE Power Engineering Review.

[30]  Yu Zhang,et al.  An active islanding detection method for grid-connected converters , 2008, 2008 3rd IEEE Conference on Industrial Electronics and Applications.