Optimized Extreme Learning Machine for Power System Transient Stability Prediction Using Synchrophasors
This work addresses transient stability prediction for power systems, but it is incremental as it combines existing techniques like ELM and IPSO.
The paper tackles power system transient stability prediction by developing an optimized extreme learning machine method using synchrophasors, achieving verified correctness and validity on IEEE 39-bus and real large-scale systems.
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.