期刊
IET SCIENCE MEASUREMENT & TECHNOLOGY
卷 11, 期 4, 页码 504-515出版社
INST ENGINEERING TECHNOLOGY-IET
DOI: 10.1049/iet-smt.2016.0432
关键词
signal classification; fuzzy set theory; Hilbert transforms; mathematics computing; distributed power generation; IEC standards; MATLAB-Simulink environment; standard IEC microgrid model; distributed generation-based microgrid; short-time modified Hilbert transform; fuzzy assessment tree; signal classification; multiclass disturbance detection; advanced signal processing techniques
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.
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