Not only do modeling and simulation help provide a better understanding of how real-world
systems function, they also enable us to predict system behavior before a system is actually …

The modern science is still striving to develop consciousness-based machine. In the last
century, enormous industrial and technological developments had taken place. Technology …

Abstract Application of Artificial Neural Networks (ANNs) for electrical load forecasting has
been proposed in the literature. ANNs have some inherent drawbacks and limitations, such …

An adaptive fuzzy logic power system stabilizer (AFPSS) consisting of a generalized neuron
(GN)-based predictor and a fuzzy logic controller (FLC) is described. The inference …

This paper presents two different power system stabilizers (PSSs) which are designed
making use of neural fuzzy network and genetic algorithms (GAs). In both cases, GAs tune a …

Artificial neural networks can be used as intelligent controllers to control nonlinear, dynamic
systems through learning, which can easily accommodate the nonlinearities and time …

Electric power grid is a widely distributed system, consisting of dispersed generators
interconnected through transmission lines, mounting real and reactive power compensators …

This study presents a new concept to control a distribution static compensator (DSTATCOM)
based on generalised neural network in a three‐phase power distribution system. Artificial …

A hybrid and adaptive control approach for solar photovoltaic system and fuel cell fed
voltage source converter (VSC) is presented in this work. Further maximum power from solar …

Feedforward neural networks such as multilayer perceptrons (MLP) and recurrent neural
networks are widely used for pattern classification, nonlinear function approximation, density …