| تعداد نشریات | 31 |
| تعداد شمارهها | 848 |
| تعداد مقالات | 8,174 |
| تعداد مشاهده مقاله | 16,079,050 |
| تعداد دریافت فایل اصل مقاله | 10,605,572 |
Multi-Class Short-Term Voltage Stability Assessment Considering the Missing Data | ||
| International Journal of Industrial Electronics Control and Optimization | ||
| مقاله 3، دوره 9، شماره 2، شهریور 2026، صفحه 145-155 اصل مقاله (818.99 K) | ||
| نوع مقاله: Research Articles | ||
| شناسه دیجیتال (DOI): 10.22111/ieco.2025.52426.1697 | ||
| نویسندگان | ||
| AmirHossein Babaali؛ Mohammad Taghi Ameli* | ||
| Faculty of Electrical Engineering, Shahid Beheshti University, Tehran, Iran | ||
| چکیده | ||
| Short-term voltage stability (STVS) varies with operating conditions of power networks, making its accurate assessment a critical challenge. This paper investigates a multi-class, data-driven approach to STVS evaluation. A dynamic index is employed to categorize voltage magnitude variations into three classes: stable, alert, and unstable. A significant obstacle in data-driven methods is missing measurement data, typically caused by sensor failures or communication delays. To address this issue, we propose two complementary solutions. First, a Bidirectional Gated Recurrent Unit (Bi-GRU) network with an attention mechanism is designed to recover data loss due to sensor failures. This method leverages both temporal trends and historical system information to reconstruct missing values with high accuracy. Second, a variable-length sliding window (VLSW) algorithm combined with a Bi-GRU is introduced to mitigate data loss arising from communication delays. The VLSW algorithm enhances data diversity and enables fast recovery. Simulation results on IEEE 39-bus and IEEE 118-bus test systems demonstrate that the proposed framework effectively identifies multi-class STVS under missing data conditions and remains robust against long-range data losses. Finally, validation on a real-world local network further confirms the practicality and robustness of the proposed approach. | ||
| کلیدواژهها | ||
| Bidirectional gated recurrent unit (Bi-GRU)؛ communication delay؛ missing data؛ short-term voltage stability assessment (STVSA)؛ multi-class classification | ||
| مراجع | ||
|
[1] X. Liang, H. Chai, and J. Ravishankar, “Analytical Methods of Voltage Stability in Renewable Dominated Power Systems: A Review,” Electricity, vol. 3, no. 1, pp. 75–107, Feb. 2022, doi: 10.3390/electricity3010006.
[2] P. Fakhrooeian, mehrdad Abedi, and peyman karimyan, “Optimal allocation and sizing of dynamic VAR support to improve short-term voltage stability considering wind farm and dynamic load model,” IECO, vol. 1, no. 1, June 2018, doi: 10.22111/ieco.2018.24388.1024. [3] N. Khosravi, D. Çelik, H. Bevrani, and S. Echalih, “Microgrid stability: A comprehensive review of challenges, trends, and emerging solutions,” International Journal of Electrical Power & Energy Systems, vol. 170, p. 110829, Sept. 2025, doi: 10.1016/j.ijepes.2025.110829. [4] J. Cao, M. Zhang, and Y. Li, “A Review of Data-Driven Short-Term Voltage Stability Assessment of Power Systems: Concept, Principle, and Challenges,” Mathematical Problems in Engineering, vol. 2021, pp. 1–12, Dec. 2021, doi: 10.1155/2021/5920244. [5] F. Boronuosi, S. Azad, M.-T. Ameli, and M. R. Shadi, “Dynamic Security Assessment of Power Systems Using a Deep Learning and GAIN-Based Approach for Addressing Missing Data,” Results in Engineering, p. 106585, Aug. 2025, doi: 10.1016/j.rineng.2025.106585. [6] A. Xue et al., “Method of amplitude data recovery in PMU measurements that considers synchronisation errors,” IET Generation Trans & Dist, vol. 14, no. 24, pp. 5746–5755, Dec. 2020, doi: 10.1049/iet-gtd.2020.0785. [7] Y. Cheng, B. Foggo, K. Yamashita, and N. Yu, “Missing Value Replacement for PMU Data via Deep Learning Model With Magnitude Trend Decoupling,” IEEE Access, vol. 11, pp. 27450–27461, 2023, doi: 10.1109/ACCESS.2023.3254448. [8] L. Zhu, C. Lu, I. Kamwa, and H. Zeng, “Spatial–Temporal Feature Learning in Smart Grids: A Case Study on ShortTerm Voltage Stability Assessment,” IEEE Transactions on Industrial Informatics, vol. 16, no. 3, pp. 1470–1482, 2020, doi: 10.1109/TII.2018.2873605. [9] C. Ren, Y. Xu, Y. Zhang, and R. Zhang, “A Hybrid Randomized Learning System for Temporal-Adaptive Voltage Stability Assessment of Power Systems,” IEEE Transactions on Industrial Informatics, vol. 16, no. 6, pp. 3672–3684, June 2020, doi: 10.1109/TII.2019.2940098. [10] J. Zhang, Y. Luo, B. Wang, C. Lu, J. Si, and J. Song, “Deep Reinforcement Learning for Load Shedding Against Short-Term Voltage Instability in Large Power Systems,”IEEE Transactions on Neural Networks and Learning Systems, vol. 34, no. 8, pp. 4249–4260, Aug. 2023, doi: 10.1109/TNNLS.2021.3121757. [11] L. Zhu, D. J. Hill, and C. Lu, “Intelligent Short-Term Voltage Stability Assessment via Spatial Attention Rectified RNN Learning,” IEEE Transactions on Industrial Informatics, vol. 17, no. 10, pp. 7005–7016, Oct. 2021, doi: 10.1109/TII.2020.3041300. [12] G. Wang, Z. Zhang, Z. Bian, and Z. Xu, “A short-term voltage stability online prediction method based on graph convolutional networks and long short-term memory networks,” International Journal of Electrical Power & Energy Systems, vol. 127, pp. 106647–106647, 2021, doi: 10.1016/j.ijepes.2020.106647. [13] Y. Luo, C. Lu, L. Zhu, and J. Song, “Data-driven shortterm voltage stability assessment based on spatialtemporal graph convolutional network,” International Journal of Electrical Power & Energy Systems, vol. 130, pp. 106753–106753, Sept. 2021, doi: 10.1016/j.ijepes.2020.106753. [14] H. Cai and D. J. Hill, “A data-driven distributed and easyto-transfer method for short-term voltage stability assessment,” International Journal of Electrical Power & Energy Systems, vol. 139, pp. 107960–107960, 2022, doi: 10.1016/j.ijepes.2022.107960. [15] Y. Li, M. Zhang, and C. Chen, “A Deep-Learning intelligent system incorporating data augmentation for Short-Term voltage stability assessment of power systems,” Applied Energy, vol. 308, pp. 118347–118347, 2022, doi: 10.1016/j.apenergy.2021.118347. [16] C. Ren, X. Du, Y. Xu, Q. Song, Y. Liu, and R. Tan, “Vulnerability Analysis, Robustness Verification, and Mitigation Strategy for Machine Learning-Based Power System Stability Assessment Model Under Adversarial Examples,” IEEE Transactions on Smart Grid, vol. 13, no. 2, pp. 1622–1632, 2022, doi: 10.1109/TSG.2021.3133604. [17] A. Liu, X. Guan, D. Sun, H. Jiang, C. Cui, and D. Fang, “Short Term Voltage Stability Assessment with Incomplete Data Based on Deep Reinforcement Learning in the Internet of Energy,” in Wireless Algorithms, Systems, and Applications, vol. 12938, Z. Liu, F. Wu, and S. K. Das, Eds., in Lecture Notes in Computer Science, vol. 12938. , Cham: Springer International Publishing, 2021, pp. 225–236. doi: 10.1007/978-3-030-86130-8_18. [18] B. Ma, J. Yang, X. Peng, K. Jiang, D. Liu, and K. Cao, “An adaptive assessment method of power system transient stability considering PMU data loss,” IET Generation Trans & Dist, vol. 18, no. 24, pp. 4116–4133, Dec. 2024, doi: 10.1049/gtd2.13340. [19] T. Luo and X. Jiang, “A novel multi-task learning method for evaluating short-term voltage stability with incomplete PMU measurements,” Complex Intell. Syst., vol. 10, no. 2, pp. 1971–1983, Apr. 2024, doi: 10.1007/s40747-023-01252-8.
[20] L. Zhu et al., “Robust Representation Learning for Power System Short-Term Voltage Stability Assessment Under Diverse Data Loss Conditions,” IEEE Transactions on Neural Networks and Learning Systems, vol. 35, no. 5, pp. 6035-6047, May 2024, doi: 10.1109/TNNLS.2023.3325542. [21] B. Tan et al., “Spatial-temporal adaptive transient stability assessment for power system under missing data,” International Journal of Electrical Power & Energy Systems, vol. 123, pp. 106237–106237, 2020, doi: 10.1016/j.ijepes.2020.106237. [22] K. Sun, M. Huang, Z. Wei, and G. Sun, “High-RefreshRate Robust State Estimation Based on Recursive Correction for Large-Scale Power Systems,” IEEE Trans. Instrum. Meas., vol. 72, pp. 1–13, 2023, doi: 10.1109/TIM.2023.3277070. [23] D. Osipov and J. H. Chow, “PMU Missing Data Recovery Using Tensor Decomposition,” IEEE Trans. Power Syst., vol. 35, no. 6, pp. 4554–4563, Nov. 2020, doi: 10.1109/TPWRS.2020.2991886. [24] B. Foggo and N. Yu, “Online PMU Missing Value Replacement Via Event-Participation Decomposition,” IEEE Transactions on Power Systems, vol. 37, no. 1, pp. 488–496, Jan. 2022, doi: 10.1109/TPWRS.2021.3093521. [25] Y. Cheng, B. Foggo, K. Yamashita, and N. Yu, “Missing Value Replacement for PMU Data via Deep Learning Model With Magnitude Trend Decoupling,” IEEE Access, vol. 11, pp. 27450–27461, 2023, doi: 10.1109/ACCESS.2023.3254448. [26] Z. Yang, H. Liu, T. Bi, Z. Li, and Q. Yang, “An adaptive PMU missing data recovery method,” International Journal of Electrical Power & Energy Systems, vol. 116, pp. 105577–105577, 2020, doi: 10.1016/j.ijepes.2019.105577. [27] J. Pei, Z. Wang, J. Wang, and D. Shi, “Robust fast PMU measurement recovery enhanced by randomized singular value and sequential Tucker decomposition,” IET Generation Trans & Dist, vol. 16, no. 16, pp. 3267–3281, Aug. 2022, doi: 10.1049/gtd2.12520. [28] X. Deng et al., “Deep learning model to detect various synchrophasor data anomalies,” IET Generation Trans & Dist, vol. 14, no. 24, pp. 5739–5745, Dec. 2020, doi: 10.1049/iet-gtd.2020.0526. [29] Z. Niu, G. Zhong, and H. Yu, “A review on the attention mechanism of deep learning,” Neurocomputing, vol. 452, pp. 48–62, Sept. 2021, doi: 10.1016/j.neucom.2021.03.091. [30] Y.-F. Zhang, P. J. Thorburn, W. Xiang, and P. Fitch, “SSIM—A Deep Learning Approach for Recovering Missing Time Series Sensor Data,” IEEE Internet of Things Journal, vol. 6, no. 4, pp. 6618–6628, Aug. 2019, doi: 10.1109/JIOT.2019.2909038. [31] M. M. Ahmed, M. Amjad, M. A. Qureshi, K. Imran, Z. M. Haider, and M. O. Khan, “A Critical Review of State-ofthe-Art Optimal PMU Placement Techniques,” Energies, vol. 15, no. 6, p. 2125, Mar. 2022, doi: 10.3390/en15062125. [32] A. H. Babaali and M. T. Ameli, “Weighted ensemble learning for real‐time short‐term voltage stability assessment with phasor measurements data,” IET Generation, Transmission & Distribution, Mar. 2023, doi: 10.1049/gtd2.12809. [33] A. Babaali and M. T. Ameli, “Short-term voltage stability prediction based on a Bidirectional Gated Recurrent Unit neural network using phasor measurement data in power systems,” Computational Intelligence in Electrical Engineering, vol. 15, no. 1, pp. 1–16, 2024, doi: 10.22108/isee.2023.135273.1585. [34] S. Shah, S. Koley, and F. Malandra, “Experimental EndTo-End Delay Analysis of LTE cat-M With High-Rate Synchrophasor Communications,” July 11, 2022, arXiv: arXiv:2207.04847. doi: 10.48550/arXiv.2207.04847. | ||
|
آمار تعداد مشاهده مقاله: 314 تعداد دریافت فایل اصل مقاله: 181 |
||