Hybrid Attack Optimization Supported Enhanced Deep Learning to Facilitate Power System Event Detection using PMU Data
Keywords:
Deep Convolution Neural Network (DCNN), event detection, Hybrid Attack Optimization (HAO), Long Short-Term Memory (LSTM), Phasor Measurement Unit (PMU).Abstract
Accurate event detection is crucial for initiating control and protection measures in power systems to ensure enhanced and reliable operation. Phasor measurement units (PMUs) play a vital role in various functional aspects of power systems, including state estimation and intelligent protection algorithms. However, the authenticity of real-time data from PMUs must be verified before feeding it into applicable real-time algorithms to prevent undesirable or erroneous operations. This paper aims to present an efficient preprocessing methodology for identifying unwanted, incorrect, missing, or noisy PMU data to facilitate robust event detection algorithms. The proposed methodology leverages real-time data-driven deep learning techniques for authenticating incoming data. Given the high sampling rate of PMUs, the presence of extraneous data can lead to false event detection, necessitating reliable data preprocessing. Challenges identified in existing literature, such as the limitations of Steady State (SS)-Local Outlier Factor (LOF) in event detection and classification, issues with detecting line tripping and inter-area oscillations, computational and bandwidth requirements for micro-PMU installations, and false alarms resulting from inaccuracies in frequency ramp rate determination, are addressed. To overcome these challenges, this research proposes a deep learning approach that utilizes modified Deep Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) classifiers to classify and extract features from PMU data, enabling highly efficient detection of disturbances in transmission lines. Additionally, a hybrid attack optimization (HAO) technique is employed to enhance convergence rates, accuracy, and efficiency. Performance evaluation of the proposed system is conducted by calculating and assessing disturbances generated by power lines using metrics such as accuracy, precision, recall, System Average Interruption Duration Index (SAIDI), and System Average Interruption Frequency Index (SAIFI).
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S. K. M. Shaikh and M. K, “An empirical study of PMU data based power system event detection techniques,” 5th Int. Conf. on Power, Control & Embedded Systems (ICPCES), Allahabad, India: IEEE, , pp. 1–6, Jan. 2023.
A. A. Hai, T. Mohamed, M. Pavlovski, M. Kezunovic, and Z. Obradovic, “Transfer Learning on Phasor Measurement Data from a Power System to Detect Events in Another System,” 21st IEEE Int. Conf. on Machine Learning and Applications (ICMLA), Nassau, Bahamas: IEEE, , pp. 1567–1572, Dec. 2022.
M. Alqudah, M. Pavlovski, T. Dokic, M. Kezunovic, Y. Hu, and Z. Obradovic, “Fault Detection Utilizing Convolution Neural Network on Timeseries Synchrophasor Data From Phasor Measurement Units,” IEEE
Trans. Power Syst., vol. 37, no. 5, pp. 3434–3442, Sep. 2022.
W. Wang et al., “Frequency Disturbance Event Detection Based on Synchrophasors and Deep Learning,” IEEE Trans. Smart Grid, vol. 11, no. 4, pp. 3593–3605, Jul. 2020.
Y. Wen and B. Yuan, “Use CNN-LSTM network to analyze secondary market data,” in Proceedings of the 2nd Int. Conf. on Innovation in Artificial Intelligence, Shanghai China: ACM, pp. 54–58, Mar. 2018.
S. Liu et al., “Data-Driven Event Detection of Power Systems Based on Unequal-Interval Reduction of PMU Data and Local Outlier Factor,” IEEE Trans. Smart Grid, vol. 11, no. 2, pp. 1630–1643, Mar. 2020.
J. Shi, B. Foggo, X. Kong, Y. Cheng, N. Yu, and K. Yamashita, “Online Event Detection in Synchrophasor Data with Graph Signal Processing,” in IEEE Int. Conf. on Communications, Control, and Computing Tech- nologies for Smart Grids (SmartGridComm), Tempe, AZ, USA: IEEE, pp. 1–7, Nov. 2020.
S. Pouyanfar and S.-C. Chen, “Automatic Video Event Detection for Imbalance Data Using Enhanced Ensemble Deep Learning,” textitInt. J. Semantic Comput., vol. 11, no. 01, pp. 85–109, Mar. 2017.
M. Cui, J. Wang, J. Tan, A. R. Florita, and Y. Zhang, “A Novel Event Detection Method Using PMU Data With High Precision,” IEEE Trans. Power Syst., vol. 34, no. 1, pp. 454–466, Jan. 2019.
P. Dawidowski, J. Sipowicz, P. Balcerek, A. Burek, and M. Smolana, “Power System Event Detection using Auto-encoders and the Fourier Transform,” in Modern Electric Power Systems (MEPS), Wroclaw,
Poland: IEEE, pp. 1–4, Sep. 2019.
R. Yadav, A. K. Pradhan, and I. Kamwa, “Real-Time Multiple Event Detection and Classification in Power System Using Signal Energy Trans- formations,” IEEE Trans. Ind. Inform., vol. 15, no. 3, pp. 1521–1531, Mar. 2019.
D. Ma, X. Hu, H. Zhang, Q. Sun, and X. Xie, “A Hierarchical Event Detection Method Based on spectral Theory of Multidimensional Matrix for Power System,” IEEE Trans. Syst. Man Cybern. Syst., vol. 51, no. 4,
pp. 2173–2186, Apr. 2021.
S. Jadhav, S. Lavand, and G. Gajjar, “Wide Area Measurement Systembased Frequency Data Mining for Event Detection in Power System,” in 8th International Conference on Power Systems (ICPS), Jaipur, India: IEEE, pp. 1–6, Dec. 2019.
A. Aligholian, A. Shahsavari, E. Cortez, E. Stewart, and H. Mohsenian-Rad, “Event Detection in Micro-PMU Data: A Generative Adversarial Network Scoring Method,” in IEEE Power & Energy Society General
Meeting (PESGM), Montreal, QC, Canada: IEEE, Aug. 2020, pp. 1–5.
Yuxuan Yuan, YifeiGuo, KavehDehghanpour, Zhaoyu Wang and Yan-chao Wang, ” Learning-Based Real-Time Event Identification Using Rich Real PMU Data” IEEE Trans. Power Sys., pp 1-12, May 2021.
Mohammad Reza Shadi, Mohammad-TaghiAmeli, Sasan Azad, ”Areal-time hierarchical framework for fault detection, classification, and location in power systems using PMUs data and deep learning”, Int. J. Electr. Power Energy Syst., Vol. 134, 2022.
X. Xia, R. Togneri, F. Sohel, and D. Huang, “Random forest classifica-tion based acoustic event detection utilizing contextual-information and bottleneck features,” Pattern Recognit., vol. 81, pp. 1–13, Sep. 2018.
[Online]. Available: https://api.semanticscholar.org/CorpusID:60245515
F. Duan, S. Zhang, Y. Yan, and Z. Cai, “An Oversampling Method of Unbalanced Data for Mechanical Fault Diagnosis Based on MeanRadius- SMOTE,” Sensors, vol. 22, no. 14, p. 5166, Jul. 2022.
Q. Dong, J. Sun, Q. Wu, and Y. Liu, “A Method for Filtering Low Frequency Disturbance in PMU Data Before Coordinated Usage in SCADA,” IEEE Trans. Power Syst., vol. 32, no. 4, pp. 2810–2816, Jul. 2017.
“IEEE Guide for Electric Power Distribution Reliability Indices,” IEEE. doi: 10.1109/IEEESTD.2012.6209381.
D. Kristomo, R. Hidayat, and I. Soesanti, “Feature extraction and classification of the Indonesian syllables using Discrete Wavelet Transform and statistical features,” in 2nd International Conference on Science and Technology-Computer (ICST), Yogyakarta, Indonesia: IEEE, pp. 88–92, Oct. 2016.
H. Pan, M. Azimi, G. Gui, F. Yan, and Z. Lin, “Vibration-Based Support Vector Machine for Structural Health Monitoring,” in Experimental Vibration Analysis for Civil Structures, vol. 5, J. P. Conte, R. Astroza, G. Benzoni, G. Feltrin, K. J. Loh, and B. Moaveni, Eds., in Lecture Notes in Civil Engineering, vol. 5. , Cham: Springer International Publishing, pp. 167–178,2018, doi: 10.1007/978-3-319-67443-8 14.
A. A. Hai et al., “Transfer Learning for Event Detection From PMU Measurements With Scarce Labels,” IEEE Access, vol. 9, pp. 127420–127432, 2021.
A. Aligholian, A. Shahsavari, E. M. Stewart, E. Cortez, and H.Mohsenian-Rad, “Unsupervised Event Detection, Clustering, and Use Case Exposition in Micro-PMU Measurements,” IEEE Trans. Smart Grid,
vol. 12, no. 4, pp. 3624–3636, Jul. 2021
