An Ensemble Deep CNN Approach for Power Quality Disturbance Classification: A Technological Route Towards Smart Cities Using Image-Based Transfer

Mirza Ateeq Ahmed Baig, Naeem Iqbal Ratyal, Adil Amin, Umar Jamil (), Sheroze Liaquat, Haris M. Khalid () and Muhammad Fahad Zia ()
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Mirza Ateeq Ahmed Baig: Department of Electrical Engineering, Mirpur University of Science & Technology (MUST), Mirpur 10250, Pakistan
Naeem Iqbal Ratyal: Department of Electrical Engineering, Mirpur University of Science & Technology (MUST), Mirpur 10250, Pakistan
Adil Amin: Department of Electrical Engineering, Mirpur University of Science & Technology (MUST), Mirpur 10250, Pakistan
Umar Jamil: Department of Electrical and Computer Engineering, University of Texas at San Antonio (UTSA), San Antonio, TX 78249, USA
Sheroze Liaquat: Eaton Research Labs, Eaton Corporation, Golden, CO 80401, USA
Haris M. Khalid: College of Engineering and Information Technology, University of Dubai, Dubai 14143, United Arab Emirates
Muhammad Fahad Zia: Department of Electrical and Computer Engineering, American University in Dubai, Dubai 28282, United Arab Emirates

Future Internet, 2024, vol. 16, issue 12, 1-24

Abstract: The abundance of powered semiconductor devices has increased with the introduction of renewable energy sources into the grid, causing power quality disturbances (PQDs). This represents a huge challenge for grid reliability and smart city infrastructures. Accurate detection and classification are important for grid reliability and consumers’ appliances in a smart city environment. Conventionally, power quality monitoring relies on trivial machine learning classifiers or signal processing methods. However, recent advancements have introduced Deep Convolution Neural Networks (DCNNs) as promising methods for the detection and classification of PQDs. These techniques have the potential to demonstrate high classification accuracy, making them a more appropriate choice for real-time operations in a smart city framework. This paper presents a voting ensemble approach to classify sixteen PQDs, using the DCNN architecture through transfer learning. In this process, continuous wavelet transform (CWT) is employed to convert one-dimensional (1-D) PQD signals into time–frequency images. Four pre-trained DCNN architectures, i.e., Residual Network-50 (ResNet-50), Visual Geometry Group-16 (VGG-16), AlexNet and SqeezeNet are trained and implemented in MATLAB, using images of four datasets, i.e., without noise, 20 dB noise, 30 dB noise and random noise. Additionally, we also tested the performance of ResNet-50 with a squeeze-and-excitation (SE) mechanism. It was observed that ResNet-50 with the SE mechanism has a better classification accuracy; however, it causes computational overheads. The classification performance is enhanced by using the voting ensemble model. The results indicate that the proposed scheme improved the accuracy (99.98%), precision (99.97%), recall (99.80%) and F1-score (99.85%). As an outcome of this work, it is demonstrated that ResNet-50 with the SE mechanism is a viable choice as a single classification model, while an ensemble approach further increases the generalized performance for PQD classification.

Keywords: ensemble learning; deep convolution neural networks; deep learning; machine learning; power quality disturbances; revolutionary technologies; smart cities; transfer learning (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2024
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