Transfer learning based dynamic security assessment

Abstract With the increasing deployment of wide‐area monitoring systems (WAMS) and phasor measurement units (PMUs), along with artificial neural network (ANN) and high‐performance distributed computation technique for smart grid and smart metering environment, online dynamic security assessment (DSA) plays a key role for early unstable event detection on power system security. It is especially important at a post‐fault operation that the timing by DSA to detect an unstable event is critical to emergency remedial control action. However, excessive update training is one of the constraints for ANN to be effectively performed at pre‐fault and post‐fault operations on online DSA. This paper describes how transfer learning is successfully employed to shorten the training time for online DSA. It also helps to improve the validation accuracy if the training dataset of scratch ANN model is insufficient. Besides, a new approach of using the densely connected convolutional network with kernel principal component analysis (KPCA) is proposed to eliminate the traditional step of dimensionality reduction.