A power quality detection and classification algorithm based on FDST and hyper-parameter tuned light-GBM using memetic firefly algorithm

•PQ disturbances (PQDs) detection and classification using FDST and LightGBM (LGBM)•The hyper-parameters of LGBM are optimized through memetic firefly algorithm.•Performance is validated on 25 types of PQDs (both single and multiple events)•The overall detection accuracy is found to be 99.714% with...

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Bibliographic Details
Published in:Measurement : journal of the International Measurement Confederation Vol. 187; p. 110260
Main Authors: Panigrahi, Rasmi Ranjan, Mishra, Manohar, Nayak, Janmenjoy, Shanmuganathan, Vimal, Naik, Bighnaraj, Jung, Young-Ae
Format: Journal Article
Language:English
Published: London Elsevier Ltd 01.01.2022
Elsevier Science Ltd
Subjects:
Algorithms
Classification
Classifiers
Comparative studies
Disturbances
Electric power systems
Fast discrete s-trasform
Feature extraction
Genetic algorithms
Heuristic methods
IEEE-1159 standard
Light GBM
Nonlinear systems
Power quality
PSO
Sensors
Waveforms
EFB
PQ
ANN
MAT
fi
y^j
EPQ
ELM
STFT
MFA
KNN
SVM
Light GBM
DT
yj
XGBM
HHT
Classification
Λ
WT
EMD
Fast discrete s-trasform
ST
PQD
LGBM
GOSS
PSO
IEEE-1159 standard
MAF
Power quality
η
Feature extraction
FE
FDST
l(·)
ISSN:0263-2241, 1873-412X
Online Access:Get full text
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Summary:•PQ disturbances (PQDs) detection and classification using FDST and LightGBM (LGBM)•The hyper-parameters of LGBM are optimized through memetic firefly algorithm.•Performance is validated on 25 types of PQDs (both single and multiple events)•The overall detection accuracy is found to be 99.714% with noiseless condition.•The detection accuracy is also verified in different level of noisy conditions. Presently, the issue of power quality (PQ) disturbances in electrical power system has been greater than before owing to increased use of power electronics based nonlinear loads. This work has proposed a hybrid PQ detection and classification algorithm that uses fast-discrete-S-transform (FDST) as feature extraction (FE) technique and memetic firefly algorithm (MFA) based Light-gradient-boost-machine (LGBM) as a classifier. In general, 25 types of PQ signals, comprising both single and multiple disturbances, are studied considering the IEEE-1159 standard. A 3.2 kHz sampling frequency is used on ten cycles of distorted waveforms for the FE. The experimental results clearly proves the effectiveness of the proposed approach with high detection accuracy (99.714% with synthetic data and 99.66% with simulated data), less computational complexity and immune to noisy environments. To end, this work has performed a comparative study with other contemporary FE techniques and classifiers, and in addition with other previously published work.
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ISSN:0263-2241
1873-412X
DOI:10.1016/j.measurement.2021.110260