  A new optimized extreme learning machine- (ELM-) based method for power
system transient stability prediction (TSP) using synchrophasors is presented
in this paper. First, the input features symbolizing the transient stability of
power systems are extracted from synchronized measurements. Then, an ELM
classifier is employed to build the TSP model. And finally, the optimal
parameters of the model are optimized by using the improved particle swarm
optimization (IPSO) algorithm. The novelty of the proposal is in the fact that
it improves the prediction performance of the ELM-based TSP model by using IPSO
to optimize the parameters of the model with synchrophasors. And finally, based
on the test results on both IEEE 39-bus system and a large-scale real power
system, the correctness and validity of the presented approach are verified.
