Comparative Evaluation of Deep Learning CNN Techniques for Power Quality Disturbance Classification
Authors
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Electrical and Electronics Engineering, Birla Institute of Technology Mesra, Ranchi, India. ,IN
Prity Soni -
Electrical and Electronics Engineering, Birla Institute of Technology Mesra, Ranchi, India. ,INPrity Soni
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Electrical and Electronics Engineering, Birla Institute of Technology Mesra, Ranchi, India. ,INDebasmita Mondal
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Electrical and Electronics Engineering, Birla Institute of Technology Mesra, Ranchi, India. ,INPankaj Mishra
DOI:
https://doi.org/10.18311/jmmf/2023/34161Keywords:
GradCAM, Deep Learning, Power Quality Disturbances, Recurrence plot, Transfer Learning.Abstract
For the power system to be stable and reliable, power quality disturbances (PQDs) must be classified. In this work, deep learning was implemented for the purpose of categorizing PQDs. The transfer learning techniques such as ResNet-50, AlexNet, and GoogLeNet were compared and evaluated for the suitability of classifying PQD signals. Accuracy, classification probability, and explainability through GradCAM- an explainable AI technique was evaluated as a grading reference for the comparative analysis. Examination of the three criteria revealed ResNet-50 as the best among all the three architectures for classifying PQD signals since depending on the accuracy.
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References
C. Liang et al., (2022): “A Kaiser Window-Based S-Transform for Time-Frequency Analysis of Power Quality Signals,” inIEEE Transactions on Industrial Informatics, Vol.18, no.2, pp. 965-975, Feb., doi: 10.1109/TII.2021.3083240.
Y. Han, Y. Feng, P. Yang, L. Xu, and A. S. Zalhaf, (2022): “An efficient algorithm for atomic decomposition of power quality disturbance signals using convolutional neural network.” Electric Power Systems Research, Vol. 206, Art no.107790, doi: 10.1016/j.epsr.2022.107790.
P. D. Achlerkar, S. R. Samantaray, and M. Sabarimalai Manikandan, (2018): “Variational Mode Decomposition and Decision Tree Based Detection and Classification of Power Quality Disturbances in Grid-Connected Distributed Generation System.” IEEE Trans. Smart Grid, vol.9, no.4, pp.3122-3132, doi: 10.1109/ tsg.2016.2626469.
U. Singh and S. N. Singh, (2018): “Optimal Feature Selection via NSGA-II for Power Quality Disturbances Classification,” in IEEE Transactions on Industrial Informatics, vol.14, no.7, pp.2994-3002, July, doi: 10.1109/TII.2017.2773475.
K. Chen, J. Hu and J. He, “A Framework for Automatically Extracting Overvoltage Features Based on Sparse Autoencoder,” in IEEE Transactions on Smart Grid, vol.9, no.2, pp.594-604, March 2018, doi: 10.1109/TSG.2016.2558200.
K. Thirumala, M. S. Prasad, T. Jain and A. C. Umarikar, (2018): “Tunable-Q Wavelet Transform and Dual Multiclass SVM for Online Automatic Detection of Power Quality Disturbances,” in IEEE Transactions on Smart Grid, vol. 9, no. 4, pp. 3018-3028, July, doi: 10.1109/TSG.2016.2624313.
S. Jamali, A. R. Farsa, and N. Ghaffarzadeh, (2018): “Identification of optimal features for fast and accurate classification of power quality disturbances.” Measurement, vol. 116, pp. 565-574, doi: 10.1016/ j.measurement.2017.10.034.
S. Wang and H. Chen, (2019): “A novel deep learning method for the classification of power quality disturbances using deep convolutional neural network.” Applied Energy, vol. 235, pp. 1126-1140, doi: 10.1016/j.apenergy.2018.09.160.
U. Singh and S. N. Singh, (2019): “A new optimal feature selection scheme for classification of power quality disturbances based on ant colony framework.” Applied Soft Computing, vol. 74, pp. 216-225, doi: 10.1016/j.asoc.2018.10.017.
R. Igual, C. Medrano, F. J. Arcega and G. Mantescu, (2017): “Mathematical model of power quality disturbances”.
“IEEE Recommended Practice for Monitoring Electric Power Quality.” doi: 10.1109/ieeestd.2009.5154067.
A. L. Moraes and R.A.S. Fernandes, (2020): “Recurrence Plots: A Novel Feature Engineering Technique to Analyze Power Quality Disturbances,” IEEE Power & Energy Society General Meeting (PESGM), 2020, pp.1-5, doi:10.1109/ PESGM41954.2020.9281699.
A. Krizhevsky, I. Sutskever, and G. E. Hinton, (2017): “ImageNet classification with deep convolutional neural networks.” Communications of the ACM, vol. 60, no. 6, pp. 84-90, doi: 10.1145/3065386.
Kaiming He, Xiangyu Zhang, Shaoqing Ren and Jian Sun, (2015): Deep residual learning for image recognition.
R. R. Selvaraju, M. Cogswell, A. Das, R. Vedantam, D. Parikh, and D. Batra, (2017): “Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization.” 2017 IEEE International Conference on Computer Vision (ICCV), doi: 10.1109/iccv.2017.74.
A. Singh, S. Sengupta, and V. Lakshminarayanan, (2017): “Explainable Deep Learning Models in Medical Image Analysis.” Journal of Imaging, vol.6, pp. 6-52, 2020, doi: 10.3390/jimaging6060052.
