Prediction of Transient Stability Using Wide Area Measurements Based on ANN

Abstract

The concept of wide-area control and protection as an application on real-time wide-area measurement systems makes the transient stability prediction more accurate in early time after fault occurrences. The transient prediction is the first step in the dynamic control system to avoid any unwanted emergency or non-stable power system state. In this paper, an early predictionof the power system stability once the fault cleaning using real-time dynamic data collected by WAMS is proposed based on an artificial neural network (ANN). The dataset collected by the different contingency analyses on the IEEE 39 bus test system is used to train a multilayer perceptron network. Pre-fault, during- fault, and post-fault generators' speeds are fed to ANN as inputs, and the status of the overall system, either stable or not, is the output of ANN. The proposed model can predict an unstable state within 100 ms after the fault. NEPLAN simulator is used to simulate the dynamic analysis ofthe IEEE 39-Bus test system, and MATLAB 2019a is used to design the ANN.