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Detection and classification of multi-complex power quality events in a smart grid using Hilbert–Huang transform and support vector machine

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Abstract

This article examines the potential ability of a chosen Hilbert–Huang transform (HHT) in detecting and identifying a multi-complex electric power quality events (PQE) signal in smart grid power systems under various noise situations. HHT is an active signal processing technique, comprising the units of empirical mode decomposition (EMD) and Hilbert spectral analysis (HSA) to detect the non-stationary electric signals. The function of EMD is to detect and decompose the non-stationary electric signal in different scales of frequency modules ranging from maximum to minimum values, and thereby the attained signals are characterized as intrinsic mode functions (IMFs). HSA details the IMF signals individually to produce a unique Hilbert spectrum which carries the information of original signal. By observing the instant time-varying deviations of frequency and amplitude of the resultant signals, it is possible to categorize the disturbing signals from the original signal. The discussed slots of work is simulated under MATLAB environment, and the results report that the HHT successfully detects the single PQE, complex PQE and multi-complex PQE signals under 25 dB, 50 dB and without noise situations. The outcomes of HHT technique are compared with other transforms such as S-transform and wavelet transform to highlight superior qualities of HHT. The identified PQE signals from HHT are classified using support vector machine to improve its classification accuracy. It is wiser to disclose that the proposed system with inbuilt monitoring and identification of PQE signals will suit present smart grid system.

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Abbreviations

ANN:

Artificial neural network

ARTMAP:

Adaptive resonance theory map

CWT:

Continuous wavelet transform

DFT:

Discrete Fourier transform

DOST:

Discrete orthogonal S-transform

ELM:

Extreme learning machine

EMD:

Empirical mode decomposition

FFT:

Fast Fourier transform

FNN:

Feed-forward neural network

FPGA:

Field programmable gate array

FT:

Fourier transform

FTTT:

Fast time–time transform

GA:

Genetic algorithm

HHT:

Hilbert–Huang transform

HST:

Hyperbolic S-transform

IMF:

Intrinsic mode functions

MNN:

Modular neural network

NM:

Not mentioned

NN:

Neural network

PDF:

Probability density functions

PNN:

Probabilistic neural network

PQ:

Power quality

PQE:

Power quality event

PSO:

Particle swarm optimization

RBDT:

Rule-based decision tree

RMS:

Root mean square

RVM:

Relevance vector machine

SAX:

Symbolic aggregate approximation

SCICA:

Single-channel independent component analysis

SPVC:

Solar photovoltaic cell

ST:

S-transform

STFT:

Short-time Fourier transform

SVM:

Support vector machine

TFAM:

Time–frequency analysis methods

WECS:

Wind energy conversion systems

WT:

Wavelet transform

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Authors and Affiliations

  1. Department of Electrical and Electronics Engineering, Saranathan College of Engineering, Tiruchchirappalli, Tamilnadu, India

    C. K. Hemapriya, M. V. Suganyadevi & C. Krishnakumar

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  1. C. K. Hemapriya
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  2. M. V. Suganyadevi
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  3. C. Krishnakumar
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Hemapriya, C.K., Suganyadevi, M.V. & Krishnakumar, C. Detection and classification of multi-complex power quality events in a smart grid using Hilbert–Huang transform and support vector machine. Electr Eng 102, 1681–1706 (2020). https://doi.org/10.1007/s00202-020-00987-8

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