Transient Stability Assessment of Electric Power System based on Voltage Phasor and CNN-LSTM

Gong, Gangjun; He, Huan; Jin, Yu-kun; Mahato, Nawaraj Kumar; Wang, Haomiao; Han, Yipin

doi:10.1109/icpsasia48933.2020.9208468

Cited by 5 publications

(2 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The power system TSA problem has been tackled by means of different deep learning architectures, to name here only a few prominent ones: convolutional neural networks (CNN) [56][57][58], recurrent neural networks (RNNs) [59], networks employing long shortterm memory (LSTM) layers [17,49,[60][61][62][63][64] or gated recurrent unit (GRU) layers [65][66][67], generative adversarial networks (GANs) [9,68,69], transfer learning [70], and autoencoders. These basic architectures can internally vary widely in the number of layers and their stacking order, which allows experimenting with different deep network topologies.…”

Section: Deep Learningmentioning

confidence: 99%

“…It would also be interesting to see if three-phase signals can be exploited as RGB channels in convolutional layers, with cross-learning between channels/phases. There are research opportunities in devising novel and better ways of converting multivariate TSA signals into images for use with very advanced deep learning image classifiers [12,55,64].…”

Section: Challenges and Future Research Opportunitiesmentioning

confidence: 99%

See 1 more Smart Citation

Artificial Intelligence Techniques for Power System Transient Stability Assessment

et al. 2022

View full text Add to dashboard Cite

The high penetration of renewable energy sources, coupled with decommissioning of conventional power plants, leads to the reduction of power system inertia. This has negative repercussions on the transient stability of power systems. The purpose of this paper is to review the state-of-the-art regarding the application of artificial intelligence to the power system transient stability assessment, with a focus on different machine, deep, and reinforcement learning techniques. The review covers data generation processes (from measurements and simulations), data processing pipelines (features engineering, splitting strategy, dimensionality reduction), model building and training (including ensembles and hyperparameter optimization techniques), deployment, and management (with monitoring for detecting bias and drift). The review focuses, in particular, on different deep learning models that show promising results on standard benchmark test cases. The final aim of the review is to point out the advantages and disadvantages of different approaches, present current challenges with existing models, and offer a view of the possible future research opportunities.

show abstract