Analysis of transients in a micro-grid using wavelet transformation

Wang, Yunqi; Ravishankar, Jayashri; Le, Phu Ngoc; Phung, B.T.

doi:10.1109/epec.2016.7771692

Cited by 2 publications

(1 citation statement)

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“…To detect power quality events, existing studies in the literature have mostly used transform-based methods like Wavelet Transform (WT), Fast Fourier Transform (FFT), S-Transform (ST), and so on [15][16][17]. Transform-based methods bring coefficients from a processed signal and need a second-level step to compose the last feature set.…”

Section: Introductionmentioning

confidence: 99%

Bundle Extreme Learning Machine for Power Quality Analysis in Transmission Networks

et al. 2019

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This paper presents a novel method for online power quality data analysis in transmission networks using a machine learning-based classifier. The proposed classifier has a bundle structure based on the enhanced version of the Extreme Learning Machine (ELM). Due to its fast response and easy-to-build architecture, the ELM is an appropriate machine learning model for power quality analysis. The sparse Bayesian ELM and weighted ELM have been embedded into the proposed bundle learning machine. The case study includes real field signals obtained from the Turkish electricity transmission system. Most actual events like voltage sag, voltage swell, interruption, and harmonics have been detected using the proposed algorithm. For validation purposes, the ELM algorithm is compared with state-of-the-art methods such as artificial neural network and least squares support vector machine.

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Section: Introductionmentioning

confidence: 99%