DATA-ANALYTICS FOR SMART GRID APPLICATIONS

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This research topic aims to utilize the various data collected from the smart meters, Phasor Measurement Units (PMUs), Power Quality (PQ) Recorders etc. to enable enhanced situational awareness, more accurate modeling, and advanced control and operation of the power system.

Specifically, our team has been developing advanced machine learning and artificial intelligence tools to extract knowledge from the smart grid data for multiple applications, including real-time data-driven power system stability assessment and control, load monitoring and modeling, uncertain variables (power load and renewable power generation) forecasting.

This research work has been funded by USA Electric Power Research Institute (EPRI), China Southern Power Grid, Ausgrid Australia, Nanyang Assistant Professorship, Rolls-Royce-NTU Corp Lab, Singtel Cognitive and Artificial Intelligence Lab (SCALE), Energy Research Institute @ NTU (ERI@N).

Selected publications in this topic:

  1. C. Ren and Y. Xu*, “Transfer Learning-based Power System Online Dynamic Security Assessment: Using One Model to Assess Many Unlearned Faults,” IEEE Trans. Power Syst., 2019.
  2. C. Ren, Y. Xu*, Y.C. Zhang, and R. Zhang, “A Hybrid Randomized Learning System for Temporal-Adaptive Voltage Stability Assessment of Power Systems,” IEEE Trans. Industrial Informatics, 2019.
  3. C. Ren and Y. Xu*, “A Fully Data-Driven Method based on Generative Adversarial Networks for Power System Dynamic Security Assessment with Missing Data,” IEEE Trans. Power Syst., 2019.
  4. Z. Yan and Y. Xu*, “Data-driven Load Frequency Control for Stochastic Power Systems: A Deep Reinforcement Learning Method with Continuous Action Search,” IEEE Trans. Power Syst., 2019.
  5. Y. Zhang, Y. Xu*, et al, “A missing-data tolerant approach for PMU-based short-term voltage stability assessment of power systems,” IEEE Trans. Smart Grid, 2019.
  6. Y. Zhang, Y. Xu*, et al, “Online power system dynamic security assessment with incomplete PMU measurements: a robust white-box model,” IET Gen. Trans. & Dist., 2019.
  7. B. Gou, Y. Xu*, et al “An Intelligent Time-adaptive Data-driven Method for Sensor Fault Diagnosis in Induction Motor Drive System,” IEEE Trans. Industrial Electronics, 2019.
  8. S. Wen, Y. Wang, Y. Tang, Y. Xu*, et al “Real-Time Identification of Power Fluctuations based on LSTM Recurrent Neural Network and A Case Study on Singapore Power System,” IEEE Trans. Industrial Informatics, 2019.
  9. W. Kong, Z.Y. Dong, Y. Jia, D.J. Hill, Y. Xu, and Y. Zhang, “Short-Term Residential Load Forecasting based on LSTM Recurrent Neural Network,” IEEE Trans. Smart Grid, 2019. – Web-of-Science Highly Cited Paper
  10. W. Kong, Z.Y. Dong, D. Hill, F. Luo, and Y. Xu, “Short-term residential load forecasting based on resident behaviour learning,” IEEE Trans. Power Syst., 2018. – Web-of-Science Highly Cited Paper
  11. Y. Zhang, Y. Xu*, et al “Real-Time Assessment of Fault-Induced Delayed Voltage Recovery: A Probabilistic Self-Adaptive Data-driven Method,” IEEE Trans. Smart Grid, 2018.
  12. Y. Zhang, Y. Xu*, et al “A Hierarchical Self-Adaptive Data-Analytics Method for Power System Short-term Voltage Stability Assessment,” IEEE Trans. Industrial Informatics, 2018.
  13. Y. Zhang, Y. Xu*, et al “Ensemble data-analytics for incomplete PMU measurement-based power system stability assessment,” IEEE Trans. Power Systems, 2018.
  14. A. Khamis, Y. Xu*, et al, “Faster detection of microgrid islanding events using an adaptive ensemble classifier,” IEEE Trans. Smart Grid, 2017.
  15. Y. Zhang, Y. Xu*, et al, “Intelligent early-warning of power system dynamic insecurity risk towards optimal accuracy-efficiency tradeoff,” IEEE Trans. Industrial Informatics, 2017.
  16. Y. Zhang, Y. Xu*, et al “Robust classification model for PMU-based on-line power system dynamic security assessment with missing data,” IET Gen. Trans. & Dist., 2017.
  17. R. Zhang, Y. Xu*, et al, “Measurement-based Composite Load Modeling using Time-Domain Simulation and Parallel-Evolutionary Search,” IET Gen. Trans. & Dist., 2016.
  18. Y. Xu*, et al, “Assessing short-term voltage stability of electric power systems by a hierarchical intelligent system,” IEEE Trans. Neural Net. & Learn. Syst., 2016.
  19. F. Luo, Z.Y. Dong, G. Chen, Y. Xu, et al, “Advanced pattern discovery based fuzzy classification method for power system dynamic security assessment,” IEEE Trans. Industrial Informatics, 2015.
  20. R. Zhang, Y. Xu*, et al, “Post-disturbance transient stability assessment of power systems by a self-adaptive intelligent system,” IET Gen. Trans. & Dist., 2015.
  21. Z.Y. Dong, Y. Xu*, et al, “Using intelligent system to assess an electric power system real-time stability,”IEEE Intelligent Systems Magazine, 2013.
  22. Y. Dai, Y. Xu, et al, “Real-time prediction of event-driven load shedding for frequency stability enhancement of power systems,” IET Gen. Trans. & Dist, 2012.
  23. Y. Xu*, et al, “An intelligent dynamic security assessment framework for power systems with wind power,” IEEE Trans. Industrial Informatics, 2012.
  24. Y. Xu, et al, “A reliable intelligent system for real-time dynamic security assessment of power systems,” IEEE Trans. Power Systems, 2012.
  25. Y. Xu, et al, “Preventive dynamic security control of power systems based on pattern discovery technique,” IEEE Trans. Power Systems, 2012.
  26. Y. Xu, et al, “Real-time transient stability assessment model using extreme learning machine,” IET Gen. Trans. & Dist., 2011.