Real-time power quality disturbance classification using convolutional neural networks

Husodo, Budi Yanto and Dalimi, Rinaldy and Ihsanto, Eko and Gunawan, Teddy Surya (2020) Real-time power quality disturbance classification using convolutional neural networks. In: Springer's Lecture Nores in Electrical Engineering (LNEE). Springer. (In Press)

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Abstract

There is a growing interest in disturbance monitoring to maintain power quality. This paper developed a real-time power quality disturbance (PQD) detection system using convolutional neural networks (CNN) due to its fast and accurate feature extraction and classification. First, 29 classes of power quality disturbance were synthetically generated around 5000 samples for each type. Second, an efficient CNN structure was developed to extract unique features. Next, the output of CNNs was then inputted into a fully connected layer with softmax and classification layer to act as the classifier for 29 classes of PQD signals. Our proposed algorithm was then trained using 80% of the synthetic signals, while 20% of the synthetic signals were used for testing. Experimental results showed that the proposed algorithm produced a good result with the classification accuracy of 97.52% trained using 100 epochs. Furthermore, it requires only 80.96 μs to detect each 16ms segment of PQD signals.

Item Type:	Book Chapter
Additional Information:	5588/84768
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:	Prof. Dr. Teddy Surya Gunawan
Date Deposited:	23 Nov 2020 09:49
Last Modified:	23 Nov 2020 10:06
URI:	http://irep.iium.edu.my/id/eprint/84768

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