August 2023
The IEEE PES Big Data Analytics subcommittee aims to drive the power system industry towards a data-driven future. The 8 task forces (TF) and working groups (WG) cover all major application areas and led by thought leaders from academia and industry. This document provides a summary of recent work and activities. More information can be found at https://cmte.ieee.org/pes-amps/subcommittees/.
Active Committees/Task Forces of Interest
- AMPS Big Data Analytics subcommittee
- Chair: Yannan Sun, , https://cmte.ieee.org/pes-amps/subcommittees/
- WG on Big Data Webinar Series
- Chair: Yang Weng, , https://www.public.asu.edu/~yweng2/Tutorial5/
- WG on Data-driven Modeling, Monitoring and Control in Power Distribution Networks
- Chair: Nanpeng Yu, , https://intra.ece.ucr.edu/~nyu/IEEE%20PES%20WG.html
- WG on Big Data & Analytics for Transmission Systems
- WG on Cloud for Grid Modernization and Digital Transformation
- Chair: Song Zhang, , https://sites.google.com/view/cloud4powergrid
- TF on Data Sharing in Energy Systems
- TF on Data Analytics for Energy Storage
- TF on Big Data & Analytics for Security and Resilience in Power Systems
- TF on Big Data Analytics for Synchro-Waveform Measurements
Technical Reports & Applicable Papers or Presentations
- “Application of spatio-temporal data-driven and machine learning algorithms for security assessment”, F.R. Segundo Sevilla, Y. Liu, E. Barocio, P. Korba et all. IEEE Technical Report PES-TR104, November 2022. https://resourcecenter.ieee-pes.org/publications/technical-reports/PES_TP_TR104_AMPS_112122.html
- “Present situation on data acquisition, handling, and analytics of operators of the transmission system in different countries and their future needs to cope with the continuous growth of data”, F.R. Segundo Sevilla, Y. Liu, E. Barocio, P. Korba et all. IEEE Technical Report PES-TR100, July 2022. https://resourcecenter.ieee-pes.org/publications/technical-reports/PES_TP_TR100_AMPS_071222.html
- “Practical Adoption of Cloud Computing in Power Systems – Drivers, Challenges, Guidance, and Real-world Use Cases”, S. Zhang, A. Pandey, X. Luo, M. Powell, R. Banerji, A. Parchure, L. Fan, E. Luzcando, IEEE PES Technical Report (PES-TR92), December 2021. https://resourcecenter.ieee-pes.org/publications/technical-reports/PES_TP_TR92_AMPS_012822.html
- “Cloud Computing for Modern Power Grid”, Song Zhang, Maggy Powell, PES University Webinar, April 2021, https://resourcecenter.ieee-pes.org/education/webinars/PES_ED_WEB_CCPG_050521_SLD.html
- PES Technical report on “Behind-The-Meter Distributed Energy Resources: Estimation, Uncertainty Quantification, and Control,” 2023. https://resourcecenter.ieee-pes.org/publications/technical-reports/PES_TP_TR111_SBLCS_60623.html
Publications
- L. Xie, X. Zheng, Y. Sun, T. Huang and T. Bruton, “Massively Digitized Power Grid: Opportunities and Challenges of Use-Inspired AI,” in Proceedings of the IEEE, vol. 111, no. 7, pp. 762-787, July 2023, doi: 10.1109/JPROC.2022.3175070. https://ieeexplore.ieee.org/document/9803820
- Z. Ma, H. Li, Y. Weng, E. Blasch, and X. Zheng, “Hd-Deep-EM: Deep Expectation Maximization for Dynamic Hidden State Recovery Using Heterogeneous Data,” IEEE Transactions on Power Systems, 2023 https://ieeexplore.ieee.org/document/10163982
- B. Saleem, Y. Weng, and Vijay Vittal, “Reduced Voltage-Dependency by Categorical Location Information and Distance Along Street Metric for Meter-Transformer Mapping in Distribution Systems,” IEEE Transactions on Power Systems, 2023 https://ieeexplore.ieee.org/document/10139840/
- H. Li, Y. Weng, Vijay Vittal, and Erik Blasch, “Distribution Grid Topology and Parameter Estimation Using Deep-Shallow Neural Network with Physical Consistency,” IEEE Transactions on Power Systems, 2023 https://ieeexplore.ieee.org/document/10138375/
- J. Yuan and Y. Weng, “Enhance Unobservable Solar Generation Estimation via Constructive Generative Adversarial Networks”, IEEE Transactions on Power Systems, 2023 https://ieeexplore.ieee.org/document/10086650/
- H. Li, Z. Ma, Y. Weng, E. Blasch, and S. Santoso, “Structural Tensor Learning for Event Identification with Limited Labels”, IEEE Transactions on Power Systems, 2023 https://ieeexplore.ieee.org/document/9996971/
- A. Ramapuram-Matavalam, K. Guddanti, and Y. Weng, “Curriculum-Based Reinforcement Learning of Grid Topology Controllers to Prevent Thermal Cascading”, IEEE Transactions on Power System, 2023 https://ieeexplore.ieee.org/document/9915475/
- P. Sundaray and Y. Weng, “Alternative Auto-Encoder for State Estimation in Distribution Systems with Unobservability”, IEEE Transactions on Smart Grid, 2023 https://ieeexplore.ieee.org/document/9880479/
- N. Enriquez and Y. Weng, “Attack Power System State Estimation by Implicitly Learning the Underlying Models”, IEEE Transactions on Smart Grid. vol. 14, no. 1, pp. 649-662, January, 2023 https://ieeexplore.ieee.org/document/9853635/
- J. Wu, J. Yuan, Y. Weng, R. Ayyanar, “Spatial-Temporal Deep Learning for Hosting Capacity Analysis in Distribution Grids”, IEEE Transactions on Smart Grid, vol. 14, no. 1, pp. 354-364, January, 2023 https://ieeexplore.ieee.org/document/9852168/
- Y. Weng, Q. Cui, and M. Guo, “Transform Waveforms into Signature Vectors for General-purpose Incipient Fault Detection”, IEEE Transactions on Power Delivery, vol 37, no. 6, pp. 4559-4569, December, 2022 https://ieeexplore.ieee.org/document/9712874/
- H. Li, Z. Ma, and Y. Weng, “A Transfer Learning Framework for Power System Event Identification”, IEEE Transactions on Power Systems, vol 37, no. 6, pp. 4424-4435, November, 2022 https://ieeexplore.ieee.org/document/9721668/
- C. Chen, X. Zheng, Y. Weng, Y. Liu, P. Guo, and L. Tai, “Adaptive Distance Protection Based on the Analytical Model of Additional Impedance for Inverter-Interfaced Renewable Power Plants During Asymmetrical Faults”, IEEE Transactions on Power Delivery, vol 37, no. 5, pp. 3823-3834, October, 2022 https://ieeexplore.ieee.org/document/9662961/
- E. Cook, S. Luo, and Y. Weng, “Solar Panel Identification via Deep Semi-supervised Learning and Deep One-Class Classification”, IEEE Transactions on Power Systems, vol. 37, no. 4, pp. 2516-2526, July, 2022 https://ieeexplore.ieee.org/document/9606553/
- J. Yuan and Y. Weng, “Support Matrix Regression for Learning Power Flow in Distribution Grid with Unobservability”, IEEE Transactions on Power Systems, vol. 37, no. 2, pp. 1151 – 1161, March, 2022 https://ieeexplore.ieee.org/document/9524510/
- Yi Wang, Qixin Chen, Tao Hong, and Chongqing Kang, “Review of Smart Meter Data Analytics: Applications, Methodologies, and Challenges,” IEEE Transactions on Smart Grid, 2019, 10(3):3125-3148. https://ieeexplore.ieee.org/document/8322199/
- Yi Wang, Imane Lahmam Bennani, Xiufeng Liu, Mingyang Sun, and Yao Zhou, “Electricity Consumer Characteristics Identification: A Federated Learning Approach,” IEEE Transactions on Smart Grid, 2021, 12(4):3637-3647. https://ieeexplore.ieee.org/document/9380668/
- Yi Wang, Mengshuo Jia, Ning Gao, Leandro Von Krannichfeldt, Mingyang Sun, and Gabriela Hug, “Federated Clustering for Electricity Consumption Pattern Extraction,” IEEE Transactions on Smart Grid, 2022, 13(3):2425-2439. https://ieeexplore.ieee.org/document/9693930/
- Yi Wang, Chien-fei Chen, Peng-Yong Kong, Husheng Li, and Qingsong Wen, “A Cyber-Physical-Social Perspective on Future Smart Distribution Systems,” Proceedings of the IEEE, 2023, 111(7):694-724. https://ieeexplore.ieee.org/document/9844442/
- D. Wu, X. Ma, T. Fu, Z. Hou, P. J. Rehm and N. Lu, “Design of a Battery Energy Management System for Capacity Charge Reduction,” in IEEE Open Access Journal of Power and Energy, vol. 9, pp. 351-360, 2022, doi: 10.1109/OAJPE.2022.3196690. https://ieeexplore.ieee.org/document/9852253
- Y. Du and D. Wu, “Deep Reinforcement Learning From Demonstrations to Assist Service Restoration in Islanded Microgrids,” in IEEE Transactions on Sustainable Energy, vol. 13, no. 2, pp. 1062-1072, April 2022, doi: 10.1109/TSTE.2022.3148236. https://ieeexplore.ieee.org/document/9705112
- Y. Li et al., “Load Profile Inpainting for Missing Load Data Restoration and Baseline Estimation,” in IEEE Transactions on Smart Grid, doi: 10.1109/TSG.2023.3293188. https://ieeexplore.ieee.org/document/10175599
- A. Das and D. Wu, “Optimal Coordination of Distributed Energy Resources Using Deep Deterministic Policy Gradient,” 2022 IEEE Electrical Energy Storage Application and Technologies Conference (EESAT), Austin, TX, USA, 2022, pp. 1-5, doi: 10.1109/EESAT55007.2022.9998046. https://ieeexplore.ieee.org/document/10066460
- H. Kim et al., “An ICA-Based HVAC Load Disaggregation Method Using Smart Meter Data,” 2023 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT), Washington, DC, USA, 2023, pp. 1-5, doi: 10.1109/ISGT51731.2023.10066402. https://ieeexplore.ieee.org/document/10066402
- X. Ma, D. Wu and A. Crawford, “Incorporating Operational Uncertainties into the Dispatch of an Integrated Solar and Storage System,” 2023 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT), Washington, DC, USA, 2023, pp. 1-5, doi: 10.1109/ISGT51731.2023.10066460. https://ieeexplore.ieee.org/document/10066460
- M. Ghafouri, M. Au, M. Kassouf, M. Debbabi, C. Assi and J. Yan, “Detection and Mitigation of Cyber Attacks on Voltage Stability Monitoring of Smart Grids,” in IEEE Transactions on Smart Grid, vol. 11, no. 6, pp. 5227-5238, Nov. 2020, doi: 10.1109/TSG.2020.3004303. https://ieeexplore.ieee.org/document/9122598
- M. Karanfil, D. E. Rebbah, M. Debbabi, M. Kassouf, M. Ghafouri, E.-N. S. Youssef, and A. Hanna, “Detection of Microgrid Cyberattacks Using Network and System Management,” in IEEE Transactions on Smart Grid, vol. 14, no. 3, pp. 2390-2405, May 2023, doi: 10.1109/TSG.2022.3218934. https://ieeexplore.ieee.org/document/9935284
- S. Zhang, A. Pandey, X. Luo, M. Powell, R. Banerji, L. Fan, A. Parchure, E. Luzcando, “Practical Adoption of Cloud Computing in Power Systems – Drivers, Challenges, Guidance, and Real-world Use Cases,” IEEE Transactions on Smart Grid, vol. 13, no. 3, May 2022, https://ieeexplore.ieee.org/document/9703493
- Fatemeh Ahmadi-Gorjayi and Hamed Mohsenian-Rad, “Data-Driven Models for Sub-Cycle Dynamic Response of Inverter-Based Resources Using WMU Measurements,” in IEEE Trans. on Smart Grid, accepted for publication, May 2023. https://ieeexplore.ieee.org/document/10136836/
- Milad Izadi and Hamed Mohsenian-Rad, “A Synchronized Lissajous-based Method to Detect and Classify Events in Synchro-waveform Measurements in Power Distribution Networks,” in IEEE Trans. on Smart Grid, vol. 13, no. 3, pp. 2170-2184, May 2022. https://ieeexplore.ieee.org/document/9702755/
- Milad Izadi and Hamed Mohsenian-Rad, “Synchronous Waveform Measurements to Locate Transient Events and Incipient Faults in Power Distribution Networks,” in IEEE Trans. on Smart Grid, vol. 12, no. 5, pp. 4295-4307, September 2021. https://ieeexplore.ieee.org/document/9432388/
- S. Liu, C. Wu, and H. Zhu, “Topology-aware graph neural networks for learning feasible and adaptive AC-OPF solutions,” IEEE Trans. on Power Systems, 2023 (in press). https://ieeexplore.ieee.org/document/9992121/
- B. Foggo, K. Yamashita and N. Yu, “pmuBAGE: The Benchmarking Assortment of Generated PMU Data for Power System Events,” in IEEE Transactions on Power Systems, doi: 10.1109/TPWRS.2023.3280430. https://ieeexplore.ieee.org/document/10141680
- Y. Cheng, N. Yu, B. Foggo and K. Yamashita, “Online Power System Event Detection via Bidirectional Generative Adversarial Networks,” in IEEE Transactions on Power Systems, vol. 37, no. 6, pp. 4807-4818, Nov. 2022, doi: 10.1109/TPWRS.2022.3153591. https://ieeexplore.ieee.org/document/9721662
- X. Kong, B. Foggo, K. Yamashita and N. Yu, “Online Voltage Event Detection Using Synchrophasor Data With Structured Sparsity-Inducing Norms,” in IEEE Transactions on Power Systems, vol. 37, no. 5, pp. 3506-3515, Sept. 2022, doi: 10.1109/TPWRS.2021.3134945.
https://ieeexplore.ieee.org/document/9648034 - B. Foggo and N. Yu, “Online PMU Missing Value Replacement Via Event-Participation Decomposition,” in IEEE Transactions on Power Systems, vol. 37, no. 1, pp. 488-496, Jan. 2022, doi: 10.1109/TPWRS.2021.3093521. https://ieeexplore.ieee.org/document/9468345
- J. Shi, B. Foggo and N. Yu, “Power System Event Identification Based on Deep Neural Network With Information Loading,” in IEEE Transactions on Power Systems, vol. 36, no. 6, pp. 5622-5632, Nov. 2021, doi: 10.1109/TPWRS.2021.3080279. https://ieeexplore.ieee.org/document/9431702
Other Available Material
- Grid Event Signature Library (GESL) at Lawrence Livermore National Laboratory (LANL) and Oak Ridge National Laboratory (ORNL) is focused on the development of curated and free-to-access power grid data repository with the goals of advancing the field of machine learning and artificial intelligence for the grid and facilitating response against grid malfunctions. GESL includes data sets for waveform measurements; contact Dr. Jhi-Young Joo () for more details.
- Hao Zhu, “Physics-aware and Risk-aware Machine Learning for Power System Operations,” PES Webinar, Feb 2022. https://resourcecenter.ieee-pes.org/education/webinars/PES_ED_WEB_PRM_021622.html