Efficient Classification of Power Quality Using Long Short-Term Memory Network Technique

Mayyadah Sahib Ibrahim; Ali Sachit Kaittan

doi:10.46604/peti.2024.13831

Authors

Mayyadah Sahib Ibrahim Department of Electrical Power and Machines, College of Engineering, University of Diyala, Baqubah, Iraq
Ali Sachit Kaittan Department of Electrical Power and Machines, College of Engineering, University of Diyala, Baqubah, Iraq

DOI:

https://doi.org/10.46604/peti.2024.13831

Keywords:

power quality, artificial intelligence, deep learning, LSTM

Abstract

This study aims to apply a new deep-learning technique to detect and categorize individual and complex PQ issues such as swell, flickers, surges, interruptions, and sags. The suggested technique, the long short-term memory (LSTM) network, is a novel artificial intelligence technique and an identifiable form of recurrent neural network. This technique is utilized to detect and identify power quality (PQ) issues based on three principal solutions: automatic feature extraction, voltage/current magnitude calculations, and PQ problem duration. Simulated PQ problems generated by the Matlab simulation and many real field data sets are used to authorize the proposed technique's capability. The real data contain voltage and current waveforms that are measured, recorded, and analyzed in medium-voltage and high-voltage (MV/HV) substations by using a data acquisition device. The simulation results show that the proposed method is capable of detecting and classifying PQ problems more accurately compared with other artificial intelligence techniques.

References

N. Singh, M. A. Ansari, M. Tripathy, and V. P. Singh, “Feature Extraction and Classification Techniques for Power Quality Disturbances in Distributed Generation: A Review,” IETE Journal of Research, vol. 69, no. 6, pp. 3836-3851, 2023.

D. H. Chiam, K. H. Lim, and K. H. Law, “LSTM Power Quality Disturbance Classification with Wavelets and Attention Mechanism,” Electrical Engineering, vol. 105, no. 1, pp. 259-266, 2023.

P. Khetarpal, N. Nagpal, P. Siano, and M. Al‐Numay, “Power Quality Disturbance Signal Segmentation and Classification Based on Modified BI‐LSTM with Double Attention Mechanism,” IET Generation, Transmission & Distribution, vol. 18, no. 1, pp. 50-62, 2024.

S. Cen, D. O. Kim, and C. G. Lim, “A Fused CNN‐LSTM Model Using FFT with Application to Real‐Time Power Quality Disturbances Recognition,” Energy Science & Engineering, vol. 11, no. 7, pp. 2267-2280, 2023.

I. S. Samanta, S. Panda, P. K. Rout, M. Bajaj, M. Piecha, V. Blazek, et al., “A Comprehensive Review of Deep-Learning Applications to Power Quality Analysis,” Energies, vol. 16, no. 11, article no. 4406, 2023.‏

X. Y. Zheng, C. P. Chen, and J. A. Jiang, “An Embedded-Based Distributed Private Cloud: Power Quality Event Classification,” Proceedings of IEEE International Conference on High Performance Computing and Communications, pp. 1237-1242, 2014.

O. P. Mahela and A. G. Shaik, “Recognition of Power Quality Disturbances Using S-Transform and Rule-Based Decision Tree,” Proceedings of IEEE 1st International Conference on Power Electronics, Intelligent Control and Energy Systems, pp. 1-6, 2016.

P. K. A. Kumar, V. J. Vijayalakshmi, J. Karpagam, and C. K. Hemapriya, “Classification of Power Quality Events Using Support Vector Machine and S-Transform,” Proceedings of 2nd International Conference on Contemporary Computing and Informatics, pp. 279-284, 2016.

S. Alshahrani, M. Abbod, and G. Taylor, “Detection and Classification of Power Quality Disturbances Based on Hilbert-Huang Transform and Feed Forward Neural Networks,” Proceedings of 51st International Universities Power Engineering Conference, pp. 1-6, 2016.

T. A. Kawady, N. I. Elkalashy, A. E. Ibrahim, and A. M. I. Taalab, “Arcing Fault Identification Using Combined Gabor Transform-Neural Network for Transmission Lines,” International Journal of Electrical Power & Energy Systems, vol. 61, pp. 248-258, 2014.

N. Köse, Ö. Salor, and K. Leblebicioğlu, “Interharmonics Analysis of Power Signals with Fundamental Frequency Deviation Using Kalman Filtering,” Electric Power Systems Research, vol. 80, no. 9, pp. 1145-1153, 2010.

A. A. Abdelsalam, A. A. Eldesouky, and A. A. Sallam, “Classification of Power System Disturbances Using Linear Kalman Filter and Fuzzy-Expert System,” International Journal of Electrical Power & Energy Systems, vol. 43, no. 1, pp. 688-695, 2012

P. C. Chang, G. W. Chang, M. H. Shih, Y. Y. Chen, Y. H. Hong, and Y. K. Yeh, “A Hybrid Approach for Detection and Classification of Power Quality Disturbances,” Proceedings of IEEE Power & Energy Society General Meeting, pp. 1-5, 2017.

F. Hafiz, S. Abecrombie, A. Eaton, C. Naik, and A. Swain, “Power Quality Event Identification Using Wavelet Packet Transform: A Comprehensive Investigation,” Proceedings of IEEE Region 10 Conference, pp. 2978-2983, 2017.

M. H. Ali, A. S. Kaittan, and M. S. Ibrahim, “Design and Deployment of a Voice Activated for Intelligent Robot,” E3S Web of Conferences, vol. 474, article no. 02007, 2024.

J. Y. Ren, J. W. Zhao, N. Pan, N. B. Zhang, and J. W. Yang, “Prediction of Distribution Network Line Loss Rate Based on Ensemble Learning,” International Journal of Engineering and Technology Innovation, vol. 14, no. 1, pp. 103-114, 2024.

A. I. Jaber, A. S. Kaittan, M. W. Abdulwahhab, and D. V. Samokhvalov, “Efficiency Improvement of PM Synchronous Wind Generator Using Field Oriented Control with Model-Base Current Observer,” International Review of Electrical Engineering, vol. 19, no. 1, pp. 22-30, 2024.

O. Ozupek, R. Yilmaz, B. Ghasemkhani, D. Birant, and R. A. Kut, “A Novel Hybrid Model (EMD-TI-LSTM) for Enhanced Financial Forecasting with Machine Learning,” Mathematics, vol. 12, no. 17, article no. 2794, 2024.

Y. Jiang, P. Dai, P. Fang, R. Y. Zhong, X. Zhao, X. Cao, et al., “A 2-LSTM for Predictive Maintenance of Industrial Equipment Based on Machine Learning,” Computers & Industrial Engineering, vol. 172, no. PA, article no. 108560, 2022.

L. Zhang, X. Zhu, S. Zhao, and D. Xu, “A Novel Virtual Network Fault Diagnosis Method Based on Long Short-Term Memory Neural Networks,” Proceedings of IEEE 86th Vehicular Technology Conference, pp. 1-5, 2017.

I. S. Samanta, S. Panda, P. K. Rout, M. Bajaj, M. Piecha, V. Blazek, et al., “A Comprehensive Review of Deep-Learning Applications to Power Quality Analysis,” Energies, vol. 16, no. 11, article no. 4406, 2023.

A. Kumar and M. N. Alam, “Bidirectional LSTM Network-Based Short-Term Load Forecasting Method in Smart Grids,” Proceedings of 5th International Conference on Energy, Power and Environment: Towards Flexible Green Energy Technologies, pp. 1-6, 2023.

L. Kirichenko, Y. Koval, S. Yakovlev, and D. Chumachenko, “Anomaly Detection in Fractal Time Series with LSTM Autoencoders,” Mathematics, vol. 12, no. 19, article no. 3079, 2024.

O. P. Mahela and A. G. Shaik, “Recognition of Power Quality Disturbances Using S-Transform Based Ruled Decision Tree and Fuzzy C-Means Clustering Classifiers,” Applied Soft Computing, vol. 59, pp. 243-257, 2017.

R. Kumar, B. Singh, and D. T. Shahani, “Symmetrical Components-Based Modified Technique for Power-Quality Disturbances Detection and Classification,” IEEE Transactions on Industry Applications, vol. 52, no. 4, pp. 3443-3450, 2016.

M. Valtierra-Rodriguez, R. Romero-Troncoso, R. A. Osornio-Rios, and A. Garcia-Perez, “Detection and Classification of Single and Combined Power Quality Disturbances Using Neural Networks,” IEEE Transactions on Industrial Electronics, vol. 61, no. 5, pp. 2473-2482, 2014.

M. Biswal and P. K. Dash, “Measurement and Classification of Simultaneous Power Signal Patterns with an S-Transform Variant and Fuzzy Decision Tree,” IEEE Transactions on Industrial Informatics, vol. 9, no. 4, pp. 1819-1827, 2013.

S. He, K. Li, and M. Zhang, “A Real-Time Power Quality Disturbances Classification Using Hybrid Method Based on S-Transform and Dynamics,” IEEE Transactions on Instrumentation and Measurement, vol. 62, no. 9, pp. 2465-2475, 2013.

J. Li, Z. Teng, Q. Tang, and J. Song, “Detection and Classification of Power Quality Disturbances Using Double Resolution S-Transform and DAG-SVMs,” IEEE Transactions on Instrumentation and Measurement, vol. 65, no. 10, pp. 2302-2312, 2016.

M. S. Manikandan, S. R. Samantaray, and I. Kamwa, “Detection and Classification of Power Quality Disturbances Using Sparse Signal Decomposition on Hybrid Dictionaries,” IEEE Transactions on Instrumentation and Measurement, vol. 64, no. 1, pp. 27-38, 2015.