Name
Abstract | ||
|---|---|---|
The increase in electric power demand pushes the modern power system for more interconnected networks. It leads to a lack of inertia and creates more critical disturbances in the power system. When this oscillation isn't damped out, it results in cascade tripping. Immediate detection of low-frequency oscillatory modes and their parameters will help the power system operator to act on a particular event without consuming much time. This research paper proposes novel strategies for identifying low-frequency modes using deep learning techniques, and the model can predict the LFO modes in different topologies. This work presents the Long Short-Term Memory Recurrent Neural Network (LSTM-RNN) approach to predict the instantaneous mode oscillatory parameters in the power system. Once the LSTM-RNN model is trained for different power disturbance situations, it can be used for any events associated with the system. Simulation results are verified using two area Kundur systems at various disturbance conditions. The simulations are performed using MATLAB software and python tensor flow library. The results are validated using statistical methods, and it confirms the superior viability and adaptability of the proposed approach in predicting the instantaneous mode parameters. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/ACCESS.2022.3149472 | IEEE ACCESS |
Keywords | DocType | Volume |
Phasor measurement units, Power system stability, Oscillators, Deep learning, Power systems, Estimation, Transforms, ANN, inter-area oscillation, low-frequency oscillation, LSTM, Teager Kaiser energy operator, variational mode decomposition | Journal | 10 |
ISSN | Citations | PageRank |
2169-3536 | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Rahul Satheesh | 1 | 0 | 0.34 |
| Nashida Chakkungal | 2 | 0 | 0.34 |
| Sunitha Rajan | 3 | 0 | 0.34 |
| Manu Madhavan | 4 | 0 | 0.34 |
| Hassan Haes Alhelou | 5 | 8 | 13.42 |