Deep Learning-Based Models for Predicting Poorly Damped Low-Frequency Modes of Oscillations

This work proposes a real-time deep learning-based model for predicting the small-signal stability of an electrical network. The trained models equip power system operators with an accurate and fast monitoring tool which can be used during online operation. To achieve this objective, three different model architectures are employed in this research; stacked long short-term memory (LSTM), convolutional neural network (CNN)-LSTM and Convectional LSTM (Conv-LSTM). These models are trained using datasets which contain the oscillatory parameters (frequency and damping ratio) of both local and inter-area modes of oscillations. In addition, the voltage phasors at different buses are taken as the model input where the output comprises the real-time oscillatory patterns of the modes. Furthermore, the overall performance of proposed models is shown for the New-England 10-machine, 39-bus, IEEE 16-machine, 68-bus, 5-area, and IEEE 50-machine, 145-bus benchmark test cases. The main findings show that training CNN-LSTM and Conv-LSTM models provide better performance compared with the stacked-LSTM model. The former models have less number of parameters and thus shorter training time. In addition, CNN_LSTM and Conv-LSTM models are less prone to overfitting problems in the network and have a better ability in capturing spatial and temporal features inherent in input data.