Advanced signal processing techniques for multiclass disturbance detection and classification in microgrids

被引：10

作者：

Chakravorti, Tatiana ^{[1
]}

Patnaik, Rajesh Kumar ^{[2
]}

Dash, Praditpta Kishor ^{[3
]}

机构：

[1] SOA Univ, Elect & Commun Engn, Bhubaneswar 751030, Orissa, India

[2] Siksha O Anusandhan Univ, Elect & Elect Engn, Bhubaneswar 769030, Orissa, India

[3] SOA Univ, Multidisciplinary Res Cell, Bhubaneswar 751024, Orissa, India

来源：

IET SCIENCE MEASUREMENT & TECHNOLOGY | 2017年 / 11卷 / 04期

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; multiclass disturbance detection; advanced signal processing techniques; AUTOMATIC CLASSIFICATION; ISLANDING PROTECTION; S-TRANSFORM; POWER; SYSTEM; RECOGNITION; GENERATION; RESOURCES; PATTERN; FOURIER;

D O I：

10.1049/iet-smt.2016.0432

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

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.

引用

页码：504 / 515

页数：12

共 27 条

[21] Automatic moment segmentation and peak detection analysis of heart sound pattern via short-time modified Hilbert transform [J].