Versatile and Robust Transient Stability Assessment via Instance Transfer Learning

To support N-1 pre-fault transient stability assessment, this paper introduces a new data collection method in a data-driven algorithm incorporating the knowledge of power system dynamics. The domain knowledge on how the disturbance effect will propagate from the fault location to the rest of the network is leveraged to recognise the dominant conditions that determine the stability of a system. Accordingly, we introduce a new concept called Fault-Affected Area, which provides crucial information regarding the unstable region of operation. This information is embedded in an augmented dataset to train an ensemble model using an instance transfer learning framework. The test results on the IEEE 39-bus system verify that this model can accurately predict the stability of previously unseen operational scenarios while reducing the risk of false prediction of unstable instances compared to standard approaches.

[1]  R D Zimmerman,et al.  MATPOWER: Steady-State Operations, Planning, and Analysis Tools for Power Systems Research and Education , 2011, IEEE Transactions on Power Systems.

[2]  Miao He,et al.  Robust Online Dynamic Security Assessment Using Adaptive Ensemble Decision-Tree Learning , 2013, IEEE Transactions on Power Systems.

[3]  Qiang Yang,et al.  A Survey on Transfer Learning , 2010, IEEE Transactions on Knowledge and Data Engineering.

[4]  Florian Thams,et al.  Deep Learning for Power System Security Assessment , 2019, 2019 IEEE Milan PowerTech.

[5]  P. Kundur,et al.  Definition and classification of power system stability IEEE/CIGRE joint task force on stability terms and definitions , 2004, IEEE Transactions on Power Systems.

[6]  Sukumar Kamalasadan,et al.  A Review of Neural Network Based Machine Learning Approaches for Rotor Angle Stability Control , 2017, ArXiv.

[7]  Yuchen Zhang,et al.  A Multiple Randomized Learning based Ensemble Model for Power System Dynamic Security Assessment , 2018, 2018 IEEE Power & Energy Society General Meeting (PESGM).

[8]  Gregor Verbic,et al.  A new dynamic security assessment framework based on semi-supervised learning and data editing , 2019, Electric Power Systems Research.

[9]  Ariel Liebman,et al.  Data-Driven Security Assessment of the Electric Power System , 2019, 2019 9th International Conference on Power and Energy Systems (ICPES).

[10]  Qiang Yang,et al.  Boosting for transfer learning , 2007, ICML '07.

[11]  Yan Xu,et al.  Transfer Learning-Based Power System Online Dynamic Security Assessment: Using One Model to Assess Many Unlearned Faults , 2020, IEEE Transactions on Power Systems.

[12]  Richard Socher,et al.  Improving Generalization Performance by Switching from Adam to SGD , 2017, ArXiv.

[13]  Joe H. Chow,et al.  Power System Dynamics and Stability: With Synchrophasor Measurement and Power System Toolbox 2e: With Synchrophasor Measurement and Power System Toolbox , 2017 .