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Power quality disturbance classification using deep BiLSTM architectures with exponentially decayed number of nodes in the hidden layers

Gunawan, Teddy Surya and Husodo, Budi Yanto and Ihsanto, Eko and Dalimi, Rinaldy (2020) Power quality disturbance classification using deep BiLSTM architectures with exponentially decayed number of nodes in the hidden layers. In: Springer's Lecture Nores in Electrical Engineering (LNEE). Springer. (In Press)

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Abstract

In recent years, there is growing interest in automatic power quality disturbance (PQD) classification using deep learning algorithms. In this paper, the average of instantaneous frequency and the average of spectrum entropy were used as time-frequency based feature extraction due to its discriminatory nature. Bidirectional Long Short-Term Memory (BiLSTM) architectures with exponentially decayed number of nodes in deep multilayers were utilized as Deep Recurrent Neural Network (DRNN) classifier. We experimentally generated fifteen classes of syn-thetic PQD signals. Each class contains 1000 samples divided randomly into training, validation, and testing. Results showed that four hidden layers of BiLSTM with exponentially decayed nodes interleaved with dropout layers pro-vided the best classification accuracy of 99.23%.

Item Type: Book Chapter
Additional Information: 5588/84769
Uncontrolled Keywords: power quality disturbance, recurrent neural network, bidirectional long short-term memory, time-frequency based feature extraction, classification.
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK7800 Electronics. Computer engineering. Computer hardware. Photoelectronic devices > TK7885 Computer engineering
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button): Kulliyyah of Engineering > Department of Electrical and Computer Engineering
Depositing User: Dr Teddy Surya Gunawan
Date Deposited: 23 Nov 2020 10:04
Last Modified: 23 Nov 2020 10:10
URI: http://irep.iium.edu.my/id/eprint/84769

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