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Power Quality Events Recognition Using S-Transform and Wild Goat Optimization-Based Extreme Learning Machine

  • Research Article - -Electrical Engineering
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Abstract

This paper presents a novel approach for automatic power quality (PQ) event detection and classification based on Stockwell transform (S-transform) and wild goat optimization (WGO)-tuned extreme learning machine (ELM). The distinctive features associated with PQ event signals have been extracted by S-transform to obtain the feature vectors characterizing the signal nature. Considering these feature vectors as input, a classifier based on ELM optimally tuned with modified WGO technique is proposed. The WGO technique originated from the social hierarchy and strategic planning to reach at peak by the wild goats in nature is adapted to formulate an effective ELM model by parameter tuning for better classification. To justify the enhanced performance of the proposed approach, it is tested on a wide range of extracted synthetic PQ event data by MATLAB simulation. To ensure the real-time implementation, the PQ event data with the addition of 20, 30, and 50 dB to the synthetic signals are considered. The analysis of results presented reveals a very high performance for both PQ event recognition and classification, ensuring the efficiency of the proposed approach.

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Abbreviations

w(d, t):

Width of the wavelet

d :

Scale parameter

f :

Frequency

S(t, f):

Stockwell transform

h(kT):

Disturbance signal

T :

Sampling time interval

N :

Number of samples

n :

Number of input neurons

l :

Number of hidden neurons

m :

Number of output neurons

h :

Hidden layer output

G(x):

Hidden layer activation function

a :

Weight matrix between input neuron and hidden neuron

β :

Weight matrix between input neuron and hidden neuron

b :

Weight of hidden neuron bias

H :

Output matrix of the hidden layer

H + :

Moore–Penrose inverse of the matrix H

wgij :

Population matrix

N wg :

Number of wild goats

N var :

Number of variables

WT:

Weight of each wg

N l :

Number of leaders

N f :

Number of followers

N g :

Number of groups

GR:

Group of wild goats

vv :

Movement vectors

P lbest :

Best leader wight value

w :

Inertia weight

R :

Personal learning coefficient

c, d :

Auxiliary parameters

wg:

Wild goat

k :

Index of the group

WTG :

Group weight value

m :

Mutation percentage

m’ :

Ratio of the current-generation iteration number to the maximum iteration number

MPi :

Total number of misclassified patterns

CL:

Class

f s :

Sampling frequency

PQ:

Power quality

WGO:

Wild goat optimization

ELM:

Extreme learning machine

STFT:

Short-time Fourier Transform

WT:

Wavelet transform

GT:

Gabor–Wigner transform

HHT:

Hilbert–Huang transform

EMD:

Empirical mode decomposition

IMF:

Intrinsic mode decomposition

HT:

Hilbert transform

VMD:

Variable mode decomposition

MM:

Mathematical morphology

AI:

Artificial intelligence

NN:

Neural network

SVM:

Support vector machine

FL:

Fuzzy logic

MP:

Moore–Penrose

WGOELM:

Wild goat optimization-based extreme learning machine

CWT:

Continuous wavelet transform

STA:

Stockwell transform amplitude

SLFN:

Single-hidden layer feed-forward neural network

EM:

Electromagnetism

PQE:

Power quality events

TF:

Time–frequency

Fstd:

Frequency standard deviation

THD:

Total harmonics distortion

ST:

Stockwell transform

SRP:

Success rate percentage

TmA:

Time–maximum amplitude

FmA:

Frequency–maximum amplitude

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Authors and Affiliations

  1. Department of Electronics and Communication Engineering, Centurion University, Odisha, India

    Indu Sekhar Samanta & Satyasis Mishra

  2. Department of Electrical and Electronics Engineering, Siksha ‘O’ Anusandhan University, Bhubaneswar, India

    Pravat Kumar Rout

Authors
  1. Indu Sekhar Samanta
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  3. Satyasis Mishra
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Correspondence to Indu Sekhar Samanta.

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Samanta, I.S., Rout, P.K. & Mishra, S. Power Quality Events Recognition Using S-Transform and Wild Goat Optimization-Based Extreme Learning Machine. Arab J Sci Eng 45, 1855–1870 (2020). https://doi.org/10.1007/s13369-019-04289-5

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