Open AccessFault‐swing discrimination using Hilbert–Huang transform integrated discrete teager energy operator
Author(s) -
Biswal Sandeep,
Biswal Monalisa
Publication year - 2018
Publication title -
iet science, measurement and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.418
H-Index - 49
eISSN - 1751-8830
pISSN - 1751-8822
DOI - 10.1049/iet-smt.2018.0053
Subject(s) - swing , hilbert–huang transform , energy operator , hilbert transform , fault detection and isolation , computer science , fault (geology) , relay , control theory (sociology) , energy (signal processing) , electronic engineering , engineering , power (physics) , artificial intelligence , white noise , mathematics , telecommunications , spectral density , statistics , actuator , physics , seismology , geology , mechanical engineering , control (management) , quantum mechanics
In this work, a fault detection methodology during power swing is demonstrated. Modern distance relays are embedded with power swing blocking (PSB) function to preserve the security and reliable operation of power system during swing. However, the operation of PSB should be unblocked and let the distance relay allow to operate for any fault during swing. However, sometimes, distance relay unable to make a proper discrimination between swing and fault event leading cascading failures. In order to accomplish the fault detection task, first online empirical mode decomposition is used and processed through Hilbert–Huang transform to compute the amplitude and instantaneous frequency of that signal. Next, discrete teager energy approach is applied to estimate the teager energy. The energy operator functions as a reliable index for fault detection task during power swing. To evaluate the performance of the proposed method, different power system structures are considered and simulated using EMTDC/PSCAD. Results for current transformer saturation, single‐pole tripping, and in presence of noise are provided. The response of the proposed method is compared with the conventional and existing methods. Results and comparative assessment reports demonstrate that the method is more efficient and robust in maintaining both selectivity and dependability.