Optimized Extreme Learning Machine for Power System Transient Stability Prediction Using Synchrophasors

TL;DR

This paper introduces an optimized extreme learning machine (ELM) approach, enhanced with improved particle swarm optimization, for accurate power system transient stability prediction using synchrophasor data, validated on IEEE and real systems.

Contribution

It presents a novel combination of ELM and IPSO to improve transient stability prediction accuracy with synchrophasor data, validated on multiple power system models.

Findings

Enhanced prediction accuracy on IEEE 39-bus system

Validated effectiveness on large-scale real power system

Improved model parameter optimization with IPSO

Abstract

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.