Short-term voltage stability prediction based on a Bidirectional Gated Recurrent Unit neural network using phasor measurement data in power systems

The development of the application of artificial intelligence and machine learning methods and the expansion of the use of phasor measurement units (PMU) have made it possible to analyze the power system as online using measurement data. In this article, a deep learning method based on a Bidirectional Gated Recurrent unit (Bi-GRU) with convolution layers is presented for online prediction of short-term voltage stability (STVS) using PMU data. In order to investigate the dynamic behavior of the power system in STVS, the database includes the time series of voltage magnitude and phase angle. A three-class classification problem (stable, alert and unstable) is defined with the help of a dynamic index including Lyapunov function and voltage deviation. The ability of bidirectional neural network in the simultaneous analysis of past and future data and the ability of the convolution layer in extracting the temporal characteristics of the data have led to an increase in the accuracy of the online STVS assessment. The simulations on IEEE 39 and 118 bus show that the proposed method can predict the voltage stability based on the measurement data pre and post-fault with good accuracy and timely, so that There will be more time for corrective actions in the network. According to the obtained results, the proposed algorithm is resistant to changing the topology of the power grid as well as changing the operating points.