The applications of PINN in PSs in recent years, including state/parameter … Contact online >>
The applications of PINN in PSs in recent years, including state/parameter
Therefore, this paper aims to provide an extensive review of recent ML
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Machine learning (ML) is one of the emerging technologies for implementing the next generation smart grid. In recent years, the PES community has witnessed significant efforts to explore the potential of machine learning for solving complex power system problems. Applications cover almost every area within the interest of PES, including generation, transmission, distribution, microgrid and customers. Also, researchers have been exploring physics-informed, performance-guaranteed, or explainable ML techniques for power systems.
IEEE PES Electrification Magazine Articles:
“Data and Cyberphysical Systems”IEEE Electrification Magazine, vol. 9, no. 1, March 2021
IEEE Power & Energy Magazine:
“Deep In Thought”Tao Hong, Arkadiusz Jedrzejewski, Spyros Chatzivasileiadis, Yan Du, Andrés M. Alonso, Lingling Fan, vol. 20, no. 3, May 2022.
IEEE Transactions on Power Systems
"Explicit Data-Driven Small-Signal Stability Constrained Optimal Power Flow”J. Liu, Z. Yang, J. Zhao, J. Yu, B. Tan and W. Li, IEEE Transactions on Power Systems, vol. 37, no. 5, pp. 3726-3737, Sept. 2022.
"Cost-Oriented Prediction Intervals: On Bridging the Gap Between Forecasting and Decision"C. Zhao, C. Wan and Y. Song, IEEE Transactions on Power Systems, vol. 37, no. 4, pp. 3048-3062, July 2022.
“Spatial Network Decomposition for Fast and Scalable AC-OPF Learning”M. Chatzos, T. W. K. Mak and P. V. Hentenryck, IEEE Transactions on Power Systems, vol. 37, no. 4, pp. 2601-2612, July 2022.
“Semi-Supervised Ensemble Learning Framework for Accelerating Power System Transient Stability Knowledge Base Generation”L. Zhu, D. J. Hill and C. Lu, IEEE Transactions on Power Systems, vol. 37, no. 3, pp. 2441-2454, May 2022.
“Data-Driven Joint Voltage Stability Assessment Considering Load Uncertainty: A Variational Bayes Inference Integrated With Multi-CNNs”M. Cui, F. Li, H. Cui, S. Bu and D. Shi, IEEE Transactions on Power Systems, vol. 37, no. 3, pp. 1904-1915, May 2022.
“Topology Identification of Distribution Networks Using a Split-EM Based Data-Driven Approach”L. Ma, L. Wang and Z. Liu, IEEE Transactions on Power Systems, vol. 37, no. 3, pp. 2019-2031, May 2022.
“A Machine Learning-Based Vulnerability Analysis for Cascading Failures of Integrated Power-Gas Systems”S. Li, T. Ding, W. Jia, C. Huang, J. P. S. Catalão and F. Li, IEEE Transactions on Power Systems, vol. 37, no. 3, pp. 2259-2270, May 2022.
“Deep Learning Based Model-Free Robust Load Restoration to Enhance Bulk System Resilience With Wind Power Penetration””J. Zhao, F. Li, X. Chen and Q. Wu, IEEE Transactions on Power Systems, vol. 37, no. 3, pp. 1969-1978, May 2022.
“Estimating Demand Flexibility Using Siamese LSTM Neural Networks”G. Ruan, D. S. Kirschen, H. Zhong, Q. Xia and C. Kang, IEEE Transactions on Power Systems, vol. 37, no. 3, pp. 2360-2370, May 2022.
“Support Matrix Regression for Learning Power Flow in Distribution Grid With Unobservability”J. Yuan and Y. Weng, IEEE Transactions on Power Systems, vol. 37, no. 2, pp. 1151-1161, March 2022.
“Neural Lyapunov Control for Power System Transient Stability: A Deep Learning-Based Approach”T. Zhao, J. Wang, X. Lu and Y. Du, IEEE Transactions on Power Systems, vol. 37, no. 2, pp. 955-966, March 2022.
“Machine Learning-Driven Virtual Bidding With Electricity Market Efficiency Analysis”Y. Li, N. Yu and W. Wang, IEEE Transactions on Power Systems, vol. 37, no. 1, pp. 354-364, Jan. 2022.
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