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

被引：0
作者：
Baig, Mirza Ateeq Ahmed [1 ]
Ratyal, Naeem Iqbal [1 ]
Amin, Adil [1 ]
Jamil, Umar [2 ]
Liaquat, Sheroze [3 ]
Khalid, Haris M. [4 ,5 ]
Zia, Muhammad Fahad [6 ]
机构：
[1] Department of Electrical Engineering, Mirpur University of Science & Technology (MUST), Mirpur
[2] Department of Electrical and Computer Engineering, University of Texas at San Antonio (UTSA), San Antonio, 78249, TX
[3] Eaton Research Labs, Eaton Corporation, Golden, 80401, CO
[4] College of Engineering and Information Technology, University of Dubai, Dubai
[5] Department of Electrical and Electronic Engineering Science, University of Johannesburg, Johannesburg
[6] Department of Electrical and Computer Engineering, American University in Dubai, Dubai
来源：
Future Internet | 2024年 / 16卷 / 12期
关键词：
deep convolution neural networks; deep learning; ensemble learning; machine learning; power quality disturbances; revolutionary technologies; smart cities; transfer learning;
D O I：
10.3390/fi16120436
中图分类号：
学科分类号：
摘要：
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. © 2024 by the authors.
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  • [21] Perez-Anaya E., Jaen-Cuellar A.Y., Elvira-Ortiz D.A., Romero-Troncoso R.J., Saucedo-Dorantes J.J., Methodology for the Detection and Classification of Power Quality Disturbances Using CWT and CNN, Energies, 17, (2024)
  • [22] Ercolano G., Rossi S., Combining CNN and LSTM for activity of daily living recognition with a 3D matrix skeleton representation, Intell. Serv. Robot, 14, pp. 175-185, (2021)
  • [23] Mohan N., Soman K., Vinayakumar R., Deep power: Deep learning architectures for power quality disturbances classification, Proceedings of the 2017 International Conference on Technological Advancements in Power and Energy (TAP Energy), pp. 1-6, (2017)
  • [24] Wang J., Zhang D., Zhou Y., Ensemble deep learning for automated classification of power quality disturbances signals, Electr. Power Syst. Res, 213, (2022)
  • [25] El-Rashidy M.A., Abd-elhamed S.A., El-Fishawy N.A., Shouman M.A., Efficient online detection system of power disturbances based on deep-learning approach, Alex. Eng. J, 70, pp. 377-394, (2023)
  • [26] Liu Y., Jin T., Mohamed M.A., A novel dual-attention optimization model for points classification of power quality disturbances, Appl. Energy, 339, (2023)
  • [27] Manikonda S.K., Santhosh J., Sreekala S.P.K., Gangwani S., Gaonkar D.N., Power quality event classification using transfer learning on images, Proceedings of the 2019 IEEE International Conference on Intelligent Techniques in Control, Optimization and Signal Processing (INCOS), pp. 1-5, (2019)
  • [28] Todeschini G., Kheta K., Giannetti C., An image-based deep transfer learning approach to classify power quality disturbances, Electr. Power Syst. Res, 213, (2022)
  • [29] Fu L., Deng X., Chai H., Ma Z., Xu F., Zhu T., PQEventCog: Classification of power quality disturbances based on optimized S-transform and CNNs with noisy labeled datasets, Electr. Power Syst. Res, 220, (2023)
  • [30] Radhakrishnan P., Ramaiyan K., Vinayagam A., Veerasamy V., A stacking ensemble classification model for detection and classification of power quality disturbances in PV integrated power network, Measurement, 175, (2021)
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