Time-Frequency Domain Analysis: Wavelet-Transform Based Fault Detection

A comprehensive analysis of wavelet-based method detecting and locating fault in HVDC system is detailed in this chapter. We study how wavelet transform can serve as a protection tool to decide right tripping signal. Detailed coefficient is one of the parameters we use to determine the fault occurrence. Using the analytical fault signal, we illustrate how the coefficient changes during pre- and post fault. There are many types of mother wavelets, for instance, Haar, Daubechies, Coiflet and Symlet. The choice of mother wavelet is a topic of interest here. Particularly, we find that Daubechies wavelet is the most optimum for fault detection as far as sensitiveness is concerned. There are some factors that can influence the performance of wavelet transform: fault type, fault resistance, fault location, choice of wavelets and sampling frequency. The simulation results show that the wavelet transform can tolerate these influences reasonably well. We use two types of simulation models: two-terminal and multi-terminal HVDC systems.

[1]  M. V. Suganyadevi,et al.  Support Vector Regression Model for the prediction of Loadability Margin of a Power System , 2014, Appl. Soft Comput..

[2]  H. Kazemi Karegar,et al.  A novel fault detection method for VSC-HVDC transmission system of offshore wind farm , 2015 .

[3]  Hongchun Shu,et al.  The Identification of Internal and External Faults for ±800kV UHVDC Transmission Line Based on Using Wavelet Based Multi-Resolution Analysis , 2010, 2010 2nd International Workshop on Database Technology and Applications.

[4]  Abhisek Ukil,et al.  Automated analysis of power systems disturbance records: Smart Grid big data perspective , 2014, 2014 IEEE Innovative Smart Grid Technologies - Asia (ISGT ASIA).

[5]  S. Mallat.  VI – Wavelet zoom , 1999 .

[6]  Abhisek Ukil,et al.  Wavelet based fault analysis in HVDC system , 2014, IECON 2014 - 40th Annual Conference of the IEEE Industrial Electronics Society.

[7]  Ahmed M. Massoud,et al.  A differential protection technique for multi-terminal HVDC , 2016 .

[8]  Abul Kalam,et al.  Generalized neural network and wavelet transform based approach for fault location estimation of a transmission line , 2014, Appl. Soft Comput..

[9]  P. Kanirajan,et al.  Power quality disturbance detection and classification using wavelet and RBFNN , 2015, Appl. Soft Comput..

[10]  S. Santoso,et al.  Power quality assessment via wavelet transform analysis , 1996 .

[11]  S. P. Valsan,et al.  Wavelet transform based digital protection for transmission lines , 2009 .

[12]  Sami Ekici,et al.  A transmission line fault locator based on Elman recurrent networks , 2009, Appl. Soft Comput..

[13]  Stéphane Mallat,et al.  A Theory for Multiresolution Signal Decomposition: The Wavelet Representation , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[14]  Ronnie Belmans,et al.  Wavelet-based protection strategy for DC faults in multi-terminal VSC HVDC systems , 2011 .

[15]  Rashmi Aspi Keswani.  Identification of Fault in HVDC Converters Using Wavelet Based Multi-Resolution Analysis , 2008, 2008 First International Conference on Emerging Trends in Engineering and Technology.

[16]  Flavio B. Costa.  Fault-Induced Transient Detection Based on Real-Time Analysis of the Wavelet Coefficient Energy , 2014 .

[17]  Geza Joos,et al.  A Combined Wavelet and Data-Mining Based Intelligent Protection Scheme for Microgrid , 2016, IEEE Transactions on Smart Grid.

[18]  Truong Q. Nguyen,et al.  Wavelets and filter banks , 1996 .

[19]  Abhisek Ukil,et al.  Current-Only Directional Overcurrent Protection for Distribution Automation: Challenges and Solutions , 2012, IEEE Transactions on Smart Grid.

[20]  Walid G. Morsi,et al.  A New Approach for Fault Classification in Microgrids Using Optimal Wavelet Functions Matching Pursuit , 2018, IEEE Transactions on Smart Grid.

[21]  Hassan Fathabadi,et al.  Two novel proposed discrete wavelet transform and filter based approaches for short-circuit faults detection in power transmission lines , 2015, Appl. Soft Comput..

[22]  Xiandong Ma,et al.  Automated wavelet selection and thresholding for PD detection , 2002 .

[23]  S. Mallat.  A wavelet tour of signal processing , 1998 .

[24]  S. Pollock,et al.  Non-Dyadic Wavelet Analysis , 2007 .