Distribution Feeder Protection Using Uncorrelated Symmetrical Dot Patterns

Distribution system protection against faults is a vital task for reliability of a power system. In the proposed work, three phase current signals obtained at substation of an IEEE 13 bus system are initially decomposed using empirical mode decomposition (EMD). The higher frequency components, Intrinsic mode functions (IMFs) of this decomposition are eliminated leaving behind a residue containing predominantly fundamental frequency. This residue being monotonic in nature, is utilized to generate visually distinct symmetrical dot patterns (SDP) for three phases. These SDPs are then correlated with those obtained under normal conditions to obtain a fault index based on alienation method. The faults are successfully detected and classified when the fault index is above a predetermined threshold. This algorithm has been successfully established for various types of faults with changing fault incidence angle, fault resistance at all the buses of the distribution system. The proposed algorithm is found to be fast in detecting and classifying faults within half cycle.

[1]  Huimin Zhao,et al.  An Approach on Fault Detection in Diesel Engine by Using Symmetrical Polar Coordinates and Image Recognition , 2014 .

[2]  Jian-Da Wu,et al.  Fault diagnosis of internal combustion engines using visual dot patterns of acoustic and vibration signals , 2005 .

[3]  Arturo Suman Bretas,et al.  System unbalance and fault impedance effect on faulted distribution networks , 2010, Comput. Math. Appl..

[4]  M. N. Rao,et al.  A wavelet based protection scheme for distribution networks with Distributed Generation , 2012, 2012 International Conference on Emerging Trends in Electrical Engineering and Energy Management (ICETEEEM).

[5]  Soumya R. Mohanty,et al.  Microgrid protection using Hilbert–Huang transform based-differential scheme , 2016 .

[6]  Songling Wang,et al.  Fan fault diagnosis based on symmetrized dot pattern analysis and image matching , 2016 .

[7]  Om Prakash Mahela,et al.  Power quality improvement in distribution network using DSTATCOM with battery energy storage system , 2016 .

[8]  Bijaya Ketan Panigrahi,et al.  High impedance fault detection in power distribution networks using time-frequency transform and probabilistic neural network , 2008 .

[9]  Manohar Mishra,et al.  Fast discrete s-transform and extreme learning machine based approach to islanding detection in grid-connected distributed generation , 2018, Energy Systems.

[10]  Geza Joos,et al.  A Combined Wavelet and Data-Mining Based Intelligent Protection Scheme for Microgrid , 2018, 2018 IEEE Power & Energy Society General Meeting (PESGM).

[11]  C A Pickover,et al.  Examining Usability, Acceptability, and Adoption of a Self-Directed, Technology-Based Intervention for Upper Limb Rehabilitation After Stroke: Cohort Study , 1986, The Journal of the Acoustical Society of America.

[12]  S.V.P. Sankar Nidadavolu,et al.  Condition monitoring of Internal Combustion engines using empirical mode decomposition and Morlet wavelet , 2009, 2009 Proceedings of 6th International Symposium on Image and Signal Processing and Analysis.

[13]  Susmita Kar,et al.  Time-frequency transform-based differential scheme for microgrid protection , 2014 .

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