Open AccessAdvanced signal processing techniques for multiclass disturbance detection and classification in microgrids
Author(s) -
Chakravorti Tatiana,
Patnaik Rajesh Kumar,
Dash Praditpta Kishor
Publication year - 2017
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.2016.0432
Subject(s) - microgrid , computer science , matlab , signal (programming language) , fuzzy logic , filter (signal processing) , pattern recognition (psychology) , multiclass classification , signal processing , artificial intelligence , digital signal processing , support vector machine , control (management) , computer vision , computer hardware , programming language , operating system
This study proposes the application of fuzzy assessment tree (FAT)‐based short‐time modified Hilbert transform (STMHT) as a new multiclass detection and classification technique, for a distributed generation (DG)‐based microgrid. The time varying non‐stationary power signal samples extracted near the target DG are initially de‐noised by passing through the morphological median filter and then processed through the proposed STMHT technique for disturbance detection. Further based on the overlapping in the target attribute values, an FAT has been incorporated, which significantly classifies the different multiclass disturbances on a standard IEC microgrid model simulated in MATLAB/Simulink environment with highest precision in accuracy.