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\begin{document}

\title{Power quality disturbance signal segmentation and classification based
on modified BI-LSTM with double attention mechanism}



\author[1]{Poras Khetarpal}%
\author[2]{Dr Neelu Nagpal}%
\author[3]{Pierluigi Siano}%
\author[4]{Mohammed Al-Numay}%
\affil[1]{Delhi Technological University Department of Electrical Engineering}%
\affil[2]{Maharaja Agrasen Institute of Technology}%
\affil[3]{University of Salerno}%
\affil[4]{King Saud University}%


\vspace{-1em}



  
  \date{June 07, 2024}


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\begin{abstract}
This paper proposes a recurrent neural network (RNN) 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 mechanism is developed. Firstly, an attention gate is
added to the basic LSTM cell to reduce the training time and focus the
memory on important PQD signal part. Secondly, attention layer is added
to the BI-LSTM to obtain the more important part of the voltage signal
by assigning weightage to the output of the BI-LSTM model. Finally a
SoftMax classifier is applied to classify the combined PQD signal in 96
different combinations. The performance of proposed BI-LSTM model with
attention gate and attention layer mechanism is compared with the
performance of baseline models based on recurrent neural network (RNN)
and convolution neural network (CNN). With this model, the PQD signal is
easily segmented from the voltage signal which makes the process of PQD
classification more accurate with less computation complexity and in
less time.%
\end{abstract}%



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