Cellular computational extreme learning machine network based frequency predictions in a power system

Frequency is one of the most important characteristics in power system monitoring, control and protection. Frequency variations can be observed with significant changes in operating conditions. High penetration levels of renewable energy pose variability and uncertainty challenges for grid operation. It is essential to have innovative methodologies to take necessary actions to overcome these challenges. Power system frequency prediction provides an insight to better system control and protection. In this paper, a cellular computational extreme learning machine network (CCELMN) based frequency prediction approach is presented. Results are compared with those obtained with independent ELM models and persistence model and shown to outperform.