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A power quality detection and classification algorithm based on FDST and hyper-parameter tuned light-GBM using memetic firefly algorithm
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DOI：10.1016/j.measurement.2021.110260.png)
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摘要
En 中文
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.
Keyword:
Power quality
Feature extraction
Classification
Fast discrete s-trasform
PSO
Light GBM
IEEE-1159 standard
期刊
IF:
5.6
论文数:
2.0W
被引数:
5.4W
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