A Real-Time Approach for Error Detection in 𝜇PMU Measurements | ||||
| JES. Journal of Engineering Sciences | ||||
| Article 3, Volume 53, Issue 1, January and February 2025, Page 1-20 PDF (1.19 MB) | ||||
| Document Type: Research Paper | ||||
| DOI: 10.21608/jesaun.2024.314227.1361 | ||||
 View on SCiNiTO | ||||
| Authors | ||||
Rahma Mohammed  ; Islam Alqabbani; Mohamed Nayel; Mansour Mohamed
| ||||
| Electrical Engineering Department, Faculty of Engineering , Assuit University, Assuit, Egypt | ||||
| Abstract | ||||
| The quality of phasor data from micro-Phasor Measurement Units (μPMUs) is critical for smart grid applications. It plays a key role in various aspects of power system management and is essential for the transition to a smarter and more sustainable grid. Recent studies imply that despite having a high level of monitoring features and accurate algorithms, μPMUs are vulnerable to errors in the measurements. Traditional methods for error detection in μPMUs typically rely on direct analysis of voltage signals. While effective to some extent, these methods can struggle with the complex and dynamic nature of power system measurements, especially under varying load conditions and in the presence of noise. To address these challenges, this paper presents a novel approach for error detection in μPMU voltage measurements using a combination of continuous wavelet transform (CWT) and a convolutional neural network (CNN). The proposed detection approach is applied on Assiut university distribution grid sub-feeder. A set of evaluation metrics such as accuracy, recall, precision, and F1 score were used to compare the error detection performance of the proposed CNN model with conventional machine learning (ML) algorithms. The results show that the proposed CNN model outperforms the conventional ML algorithms for detecting errors in μPMU voltage measurements under different load conditions. | ||||
| Keywords | ||||
| 𝜇PMU’s errors; continuous wavelet transform; convolutional neural network; feature extraction; error detection | ||||
| References | ||||
|
[1] J. A. Momoh, Smart grid: fundamentals of design and analysis, Vol. 33, John Wiley & Sons, 2012.
[2] A. E. Saldaña-González, A. Sumper, M. Aragüés-Peñalba, and M. Smolnikar, "Advanced distribution measurement technologies and data applications for smart grids: A review," Energies, vol. 13, no. 14, 3730, July 2020.
[3] A. Von Meier, E. Stewart, A. McEachern, M. Andersen and L. Mehrmanesh, "Precision micro-synchrophasors for distribution systems: A summary of applications", IEEE Transactions on Smart Grid, vol. 8, no. 6, pp. 2926-2936, 2017.
[4] M. Jamei, A. Scaglione, C. Roberts, E. Stewart, S. Peisert, C. McParland, and A. McEachern, “Anomaly detection using optimally placed µPMU sensors in distribution grids,” IEEE Trans. on Power Systems, vol. 33, no. 4, pp. 3611–3623, July 2018.
[5] Y. Zhou, R. Arghandeh, and C. J. Spanos, “Partial knowledge datadriven event detection for power distribution networks,” IEEE Trans. on Smart Grid, vol. 9, no. 5, pp. 5152–5162, Sep. 2018.
[6] A. Aligholian, A. Shahsavari, E. Cortez, E. Stewart, and H. MohsenianRad, "Event detection in micro-pmu data: A generative adversarial network scoring method." 2020 IEEE Power & Energy Society General Meeting (PESGM). IEEE, 2020. pp. 1-5.
[7] A. A. Elsayed, M. A. Abdellah, M. A. Mohamed, and M. A. E. Nayel, "µPMU Hardware and Software Design Consideration and Implementa-tion for Distribution Grid Applications," Advances in Science, Technology and Engineering Systems Journal, vol. 7, no. 4, pp. 59-71, 2022.
[8] P. A. Pegoraro, K. Brady, P. Castello, C. Muscas, and A. von Meier, "Compensation of systematic measurement errors in a PMU-based monitoring system for electric distribution grids," IEEE Transactions on Instrumentation and Measurement, vol. 68, no. 10, pp. 3871-3882, 2019.
[9] H. Zhang and A. A. Arkadan, "B–H Curve-Based Model for CT Error Evaluation," in IEEE Transactions on Magnetics, vol. 55, no. 6, pp. 1-4, June 2019.
[10] D. Shi, D. J. Tylavsky, and N. Logic, "An adaptive method for detection and correction of errors in PMU measurements," IEEE Transactions on Smart Grid, vol. 3, no. 4, pp. 1575-1583, Aug. 2012.
[11] L. Vanfretti, J. H. Chow, S. Sarawgi, and B. Fardanesh, “A phasor data-based state estimator incorporating phase bias correction,” IEEE Trans. Power Syst., vol. 26, pp. 111–119, Feb. 2011.
[12] I. N. Kolosok, E. S. Korkina, and A. E. Mahnitko, "Detection of systematic errors in PMU measurements by the power system state estimation methods," 2015 56th International Scientific Conference on Power and Electrical Engineering of Riga Technical University (RTUCON), IEEE, pp. 1-4, 2015.
[13] X. Wang, D. Shi, Z. Wang, C. Xu, Q. Zhang, X. Zhang, and Z. Yu, “Online calibration of phasor measurement unit using density-based spatial clustering,” IEEE Trans. Power Del., vol. 33, no. 3, pp. 1081–1090, Jun. 2018.
[14] K. Mahapatra, N. R. Chaudhuri, and R. Kavasseri, "Bad data detection in PMU measurements using principal component analysis," 2016 North American Power Symposium (NAPS), IEEE, pp. 1-6, 2016.
[15] J.L. Rueda, C.A. Juarez, and I. Erlich, "Wavelet-based analysis of power system low-frequency electromechanical oscillations," IEEE Transaction on Power System, vol. 26, no. 3, pp. 1733-1743, August 2011.
[16] W. Gao and J. Ning, “Wavelet-based disturbance analysis for power system wide-area monitoring,” IEEE Trans. Smart Grid, vol. 2, no. 1, pp. 121–130, Mar. 2011.
[17] I. Kolosok and L. Gurina, "Wavelet analysis of PMU measurements for identification of cyber attacks on TCMS," 2018 International Conference on Industrial Engineering, Applications and Manufacturing (ICIEAM), IEEE, 2018, pp. 1-4.
[18] K. Mahapatra, N. R. Chaudhuri, and R. Kavasseri, "Online bad data outlier detection in PMU measurements using PCA feature-driven ANN classifier," IEEE Power & Energy Society General Meeting, IEEE, pp. 1-5, 2017.
[19] M. A. Merazy, R. Hasan, M. A. Mohamed, and M. Nayel, "Error Detection in µPMU Voltage Measurements Using Artificial Neural Network," 2021 22nd International Middle East Power Systems Conference (MEPCON). IEEE, 2021, pp. 569-574.
[20] G. Khare, A. Mohapatra, and S. N. Singh, "A real-time approach for detection and correction of false data in PMU measurements," Electric Power Systems Research, vol.191, 2021, 106866.
[21] Y. Zhu, X. Xu, and Z. Yan, "Hybrid clustering‐based bad data detection of PMU measurements," Energy Conversion and Economics, vol. 2, no. 4, 2021, pp. 235-247.
[22] Z. Yang, H. Liu, T. Bi, and Q. Yang, "Bad data detection algorithm for PMU based on spectral clustering," Journal of Modern Power Systems and clean energy, vol. 8, no. 3, 2020, pp. 473-483.
[23] N. C. F. Tse, "Practical application of wavelet to power quality analysis," In: 2006 IEEE Power Engineering Society General Meeting, Montreal, Que., Canda, June 2006, pp. 5-10.
[24] F. Shiri and B. Mohammadi-ivatloo, “Identification of interarea oscillations using wavelet transform and phasor measurement unit data,” Int. Trans. Electr. Energ. Syst., 2014.
[25] E. Maggiori, Y. Tarabalka, G. Charpiat, and P. Alliez, “Convolutional neural networks for large-scale remote-sensing image classification,” IEEE Trans. Geosci. Remote Sens., vol. 55, no. 2, pp. 645–657, Feb. 2017.
[26] Y. Bengio, “Practical recommendations for gradient-based training of deep architectures,” in Neural networks: Tricks of the trade. Springer, 2012, pp. 437–478.
[27] Y. Wang, Y. Li, and X. Rong, “The influence of the activation function in a convolution neural network model of facial expression recognition,” Applied Sciences, vol. 10, no. 5, pp. 1-20. Mar. 2020.
[28] N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, “Dropout: a simple way to prevent neural networks from overfitting,” The Journal of Machine Learning Research, vol. 15, no. 1, pp. 1929–1958, 2014.
[29] A. A.Elaziez, M. A. Mohamed, M. AbdelRaheem and M. A.Nayel, "Optimal μPMU Placement and Current Channel Selection Considering Running Cost for Distribution Grid," 2020 IEEE International Conference on Power Electronics, Smart Grid and Renewable Energy (PESGRE2020), pp. 1-8, 2020.
[30] M. Sokolova and G. Lapalme, “A systematic analysis of performance measures for classification tasks,” Information processing & management, vol. 45, no. 4, pp. 427–437, 2009. | ||||
|
Statistics Article View: 261 PDF Download: 288 |
||||
