Open AccessAdvanced signal processing and machine learning techniques for voltage sag causes detection in an electric power system
Author(s) -
Mishra Manohar,
Panigrahi Rasmi Ranjan
Publication year - 2020
Publication title -
international transactions on electrical energy systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.428
H-Index - 42
ISSN - 2050-7038
DOI - 10.1002/2050-7038.12167
Subject(s) - voltage sag , hilbert–huang transform , support vector machine , electric power system , hilbert transform , computer science , pattern recognition (psychology) , artificial intelligence , artificial neural network , wavelet transform , control theory (sociology) , s transform , engineering , voltage , signal processing , discrete wavelet transform , electronic engineering , wavelet , power (physics) , digital signal processing , spectral density , power quality , computer vision , electrical engineering , quantum mechanics , physics , filter (signal processing) , telecommunications , control (management)
Summary In this manuscript, voltage sag causes (VSCs) detection in an electric power system is studied using S‐transform (ST) and variational mode decomposition (VMD) techniques. The advantages of these approaches are compared in detail against earlier implemented wavelet transform (WT), empirical mode decomposition (EMD), and Hilbert‐transform (HT) techniques, as a novel contribution to previous studies. Voltage sag can trigger periods of downtime, considerable damage of product and moreover, it can attribute to malfunctions, instabilities, and decrease the lifespan of the connected loads. Therefore, accurate detection of VSCs can avoid the loss and problems instigated by voltage sag in an electric power system. In this work, initially, the extracted disturbance voltage signals from the relaying point are passed through the aforementioned signal processing algorithms to extract unique features. Then, these extracted features are used as inputs to the extreme learning machine (ELM) classifier for recognition of VSCs. Additionally, to augment the precision of the output result, the performance of ELM classifier is compared with the artificial neural network (ANN), K‐nearest neighbors (KNN), and support vector machine (SVM) classifier. Three types of VSCs are used in this analysis such as (a) sag due to incipient fault, (b) sag due to induction motor stating, and (c) sag due to transformer energization. The simulation results show good performance and feasibility of the stated methods for VSCs recognition even under in cooperation of noisy condition.