Power quality disturbance signal segmentation and classification based on modified BI‐LSTM with double attention mechanism

Power quality disturbance signal segmentation and classification based on modified BI‐LSTM with double attention mechanism

Abstract This paper proposes a recurrent neural network based model to segment and classify multiple combined multiple power quality disturbances (PQDs) from the PQD voltage signal. A modified bi‐directional long short‐term memory (BI‐LSTM) model with two different types of attention mechanisms is d...

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Bibliographic Details
Main Authors: Poras Khetarpal, Neelu Nagpal, Pierluigi Siano, Mohammed Al‐Numay
Format: Article
Language:English
Published: Wiley 2024-01-01
Series:IET Generation, Transmission & Distribution
Subjects:
artificial intelligence
neural nets
power quality/harmonics
signal classification
signal detection
Online Access:https://doi.org/10.1049/gtd2.13065

Internet

https://doi.org/10.1049/gtd2.13065

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