An Advanced 2-Output DNN Model for Impulse Noise Mitigation in NOMA-Enabled Smart Energy Meters

Muhammad Hussain; Hina Shakir; Taimoor Zafar; Muhammad Faisal Siddiqui

Authors

Muhammad Hussain Software Engineering Department, Bahria University, Karachi, Pakistan
Hina Shakir Software Engineering Department, Bahria University, Karachi, Pakistan
Taimoor Zafar Electrical Engineering Department, Bahria University, Karachi, Pakistan
Muhammad Faisal Siddiqui

Keywords:

Deep Learning, Smart City, Peak Signal-to-Noise Ratio

Abstract

The next-generation power grid enables information exchange between consumers and suppliers through advanced metering infrastructure. However, the performance of the smart meter degrades due to impulse noise present in the power system. Besides conventional thresholding techniques, deep learning has been proposed in the literature for detecting noise in NOMA-enabled smart energy meters. This research introduces a novel Deep Neural Network (DNN) capable of simultaneously detecting and classifying impulse noise as either high or low impulse. Combining the analysis of detected noise and its class has proven to be more effective in mitigating noise compared to previously proposed methods. The input feature vector to DNN is chosen based on its characteristics to detect impulse noise and its level in the data and includes ROAD characteristics, median differences, and probability of impulse arrival. The performance evaluation shows that the Bit Error Rate (BER) of the proposed DNN is lower than the BER of single output DNN which is proposed in the literature for mitigation only. It is also shown that besides simultaneous detection and mitigation, the second output of the proposed DNN i.e. classification of IN validates the first output which is IN identification.

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