Synchrophasor Driven Voltage Stability Assessment Using Adaptive Deep Learning Based Tools on Temporal Ensembling and Data Augmentation

International Journal of Electrical and Electronics Engineering

© 2024 by SSRG - IJEEE Journal

Volume 11 Issue 12

Year of Publication : 2024

Authors : Kunal Samad, Arunkumar Patil, Katkar Siddhant Satyapal, Santosh Diggikar, N. Mohan

10.14445/23488379/IJEEE-V11I12P103

How to Cite?

Kunal Samad, Arunkumar Patil, Katkar Siddhant Satyapal, Santosh Diggikar, N. Mohan, "Synchrophasor Driven Voltage Stability Assessment Using Adaptive Deep Learning Based Tools on Temporal Ensembling and Data Augmentation," SSRG International Journal of Electrical and Electronics Engineering, vol. 11,  no. 12, pp. 26-35, 2024. Crossref, https://doi.org/10.14445/23488379/IJEEE-V11I12P103

Abstract:

This research explores the application of Phasor Measurement Units (PMUs) and deep learning methodologies to predict voltage stability by enhancing the efficient operation of the power grid. The study addresses the challenges of topological changes and contingency labeling in power system networks. The methodology incorporates generative artificial intelligence and semi-supervised learning by significantly improving the predictive accuracy of the various learning models. Least Squares Generative Adversarial Networks (LSGANs) strategically augment the training dataset, expanding both the feature space and temporal domain. The enriched dataset enables more accurate classification and enhances the model's reliability under previously unseen dynamic time-series signatures. The Recurrent Neural Network (RNN) based Temporal Ensembling improves the model's ability to determine voltage stability by clustering time signatures based on signal transitions and temporal dynamics. The deep learning model is applied to PMU time series data, which undergoes systematic evaluation criteria using various data preparation stages. In addition, the study explores multiple network topologies for the model's adaptability, testing across diverse time windows and signal conditions. Also, Hyperparameter tuning of the nominated model optimizes the performance through cross-validation, and the best configurations for the best settings were ranked based on the test scores. The findings underscore the potential of artificial intelligence and machine learning models to enhance power system stability. Such forecasts can support proactive decision-making, improve power system operations, and lay the foundation for future advancements in wide-area power system monitoring and control.

Keywords:

Voltage stability, Phasor Measurement Units (PMUs), Deep Learning, Least Square Generative Adversarial Networks (LSGANs), Recurrent Neural Network (RNNs), Temporal ensembling, Data augmentation.

References:

[1] Chao Ren et al., “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, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[2] Meng Zhang et al., “Deep Learning for Short-Term Voltage Stability Assessment of Power Systems,” IEEE Access, vol. 9, pp. 29711-29718, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[3] Lipeng Zhu, David J. Hill, and Chao 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, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[4] Shu Liu et al., “Improved Model Predictive Dynamic Voltage Cooperative Control Technology Based on PMU,” Frontiers in Energy Research, vol. 10, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[5] Manish K. Singh et al., “Physic-Informed Transfer Learning for Voltage Stability Margin Prediction,” 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Rhodes Island, Greece, pp. 1-5, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[6] Yang Li et al., “PMU Measurements-Based Short-Term Voltage Stability Assessment of Power Systems via Deep Transfer Learning,” IEEE Transactions on Instrumentation and Measurement, vol. 72, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[7] Mohamad Khairuzzaman Mohamad Zamani, Ismail Musirin, and Saiful Izwan Suliman, “Convolutional Neural Network for Voltage Stability Prediction in Power System Operation,” International Journal of Emerging Trends in Engineering Research, vol. 8, no. 1.1, pp. 205-212, 2020.
[CrossRef] [Publisher Link]
[8] Haosen Yang et al., “Deep Learning Architecture for Voltage Stability Evaluation in Smart Grid Based on Variational Autoencoders,” arXiv Preprint, 2018.
[Google Scholar]
[9] Tong Wu, Ying-Jun Angela Zhang, and He Wen, “Voltage Stability Monitoring Based on Disagreement-Based Deep Learning in a Time-Varying Environment,” IEEE Transactions on Power Systems, vol. 36, no. 1, pp. 28-38, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[10] Wanjun Huang, Weiye Zheng, and David J. Hill, “Distribution Network Reconfiguration for Short-Term Voltage Stability Enhancement: An Efficient Deep Learning Approach,” IEEE Transactions on Smart Grid, vol. 12, no. 6, pp. 5385-5395, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[11] Yuanyuan Shi et al., “Stability Constrained Reinforcement Learning for Real-Time Voltage Control,” 2022 American Control Conference (ACC), Atlanta, GA, USA, pp. 2715-2721, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[12] Syafiq Kamarul Azman et al., “A Unified Online Deep Learning Prediction Model for Small Signal and Transient Stability,” IEEE Transactions on Power Systems, vol. 35, no. 6, pp. 4585-4598, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[13] Zijian Feng et al., “Transferable Deep Learning Power System Short-Term Voltage Stability Assessment with Physics-Informed Topological Feature Engineering,” arXiv Preprint, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[14] Ananta Adhikari, Sumate Naetiladdanon, and Anawach Sangswang, “Real-Time Short-Term Voltage Stability Assessment Using Temporal Convolutional Neural Network,” 2021 IEEE PES Innovative Smart Grid Technologies - Asia (ISGT Asia), Brisbane, Australia, pp. 1-5, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[15] Ramij R. Hossain, Qiuhua Huang, and Renke Huang, “Graph Convolutional Network-Based Topology Embedded Deep Reinforcement Learning for Voltage Stability Control,” IEEE Transactions on Power Systems, vol. 36, no. 5, pp. 4848-4851, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[16] C.X. Jiang et al., “Power System Emergency Control to Improve Short-Term Voltage Stability Using Deep Reinforcement Learning Algorithm,” 2019 IEEE 3rd International Electrical and Energy Conference (CIEEC), Beijing, China, pp. 1872-1877, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[17] Zhijie Nie et al., “Adaptive Online Learning with Momentum for Contingency-Based Voltage Stability Assessment,” 2019 IEEE Power & Energy Society General Meeting (PESGM), Atlanta, GA, USA, pp. 1-5, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[18] Tayo Uthman Badrudeen, Nnamdi I. Nwulu, and Saheed Lekan Gbadamosi, “Neural Network Based Approach for Steady-State Stability Assessment of Power Systems,” Sustainability, vol. 15, no. 2, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[19] Jongju Kim et al., “Transient Stability Assessment Using Deep Transfer Learning,” IEEE Access, vol. 11, pp. 116622-116637, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[20] Wanjun Huang, and Changhong Zhao, “Deep-Learning-Aided Voltage-Stability-Enhancing Stochastic Distribution Network Reconfiguration,” IEEE Transactions on Power Systems, vol. 39, no. 2, pp. 2827-2836, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[21] Guru Mohan Baleboina, and R. Mageshvaran, “A Survey on Voltage Stability Indices for Power System Transmission and Distribution Systems,” Frontiers in Energy Research, vol. 11, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[22] Yan Chen et al., “Transient Voltage Stability Assessment and Margin Calculation Based on Disturbance Signal Energy Feature Learning,” Frontiers in Energy Research, vol. 12, 2024.