Enhanced Memetic Algorithm-Based Extreme Learning Machine Model for Smart Grid Stability Prediction

Abstract

The smart grid is considered a conventional application domain of cyber-physical system (CPS) tools in the electrical utility industry. The physical system dynamics of SG with the assistance of CPS are generally controlled by connected sensors and controllers via a communication link. These CPSs, which rely heavily on an expansive communication network and intelligent computing algorithms, are susceptible to cyber-physical attacks and are also sensitive to various technical, economical, and social factors compromising their stability. Assessment and prediction of the stability of CPSs are very vital in this context. In this work, a novel optimized (memetic algorithm-based) extreme learning machine model for smart grid-CPS stability prediction has been proposed. Here, the teaching-learning-based optimization and simulated annealing techniques are used to design the memetic algorithm. The experimental result regarding the proposed model is then compared with other contemporary machine learning and deep learning models.