ANN Robot Energy Modeling

Introductory Chapter: Electric Machines for Smart Grids and Electric Vehicles Applications

“…This book has recent advances of modeling, control, optimization, analysis, and design of electric machines especially for smart grid applications and electric vehicles. However, the editor himself has some contributions for different types of electric machines in many applications as the following: electric machines (EM) in nano-power grids [5]; EM in smart home systems [6]; performance improvement for induction motor [7]; PM synchronous machine new aspects for modeling, control, and design [8]; sizing high-speed micro-generators for smart grid systems [9]; high-speed PM generator for optimized sizing based on particle swarm for smart grids [10]; EM in small-scale hydropower generator electrical system modeling [11]; for ANN robot energy modeling [12]; smallscale wind power dispatchable energy source modeling [13]; PMSM sensorless speed control drive [14]; ANN interior PM synchronous machine performance improvement unit [15]; PMSM performance improvement [16]; sizing a high-speed PM generator for green energy applications [17]; 400 kW six analytical high-speed generator designs for smart grid systems [18]; nonlinear global sizing of high-speed PM synchronous generator for renewable energy applications [19]; micro-generator design for smart grid system (comparative study) [20]; PM synchronous motor drive system for automotive applications [21]; high-speed PM synchronous machine [22]; PM synchronous motor dynamic modeling with genetic algorithm performance improvement [23]; spacecraft flywheel high-speed PM synchronous motor design (classical and genetic) [24]; high fundamental frequency PM synchronous motor design neural regression function [25]; PM synchronous motor control strategies with their neural network regression functions [26]; highspeed PM generator optimized sizing based on particle swarm optimization for smart grids [27]; energy modeling of differential drive robots [28]; high-speed micro-turbine modeling and control for micro-grid applications [29]; optimized sizing of high-speed PM generator for renewable energy applications [30]; PM synchronous motor genetic algorithm performance improvement for renewable energy applications [...…”

Introductory Chapter: Artificial Neural Networks

“…The editor himself has used ANN in different applications such as smart distributed generation systems [5], photovoltaic module and horizontal axis wind turbine modeling [6], wind energy estimation functions for future homes [7], small-scale hydropower generator electrical system modelling [8], robot energy modeling [9], small-scale wind power dispatchable energy source modeling [10], optimum ANN empirical model of capacitive deionization desalination unit [11], lead acid battery modeling for PV applications [12], solar panel modeling-based design technique for distributed generation applications [13], wind turbine (horizontal and vertical) design and simulation aspects for renewable energy applications [14], neural network storage unit parameter modeling [15], empirical capacitive deionization ANN nonparametric modeling for desalination purpose [16], PV module optimum operation modeling [17], ANN interior PM synchronous machine performance improvement unit [18], DC-DC converter duty cycle ANN estimation for DG applications [19], stand-alone PV system simulation for DG applications-Part I: PV module modeling and inverters [20], stand-alone PV system simulation for DG applications-Part II: DC-DC converter feeding maximum power to resistive load [21], maximum power point genetic identification function for photovoltaic system [22], PV cell module modeling and ANN simulation for smart grid applications [23], a neuro-modelling for new biological technique of water pollution control [24], high fundamental frequency PM synchronous motor design neural regression function [25], PM synchronous motor control strategies with their neural network regression functions [26], DC micro-grid pricing and market models [27], battery degradation model based on ANN regression function for EV applications [28], sizing residential photovoltaic systems in the state of georgia [29], an artificial neural network model for wind energy estimation [30], site wind energy appraisal function for future egyptian homes [31], horizontal axis wind turbines modeling…”