The data are collected and forwarding it to the goal is a significant function of a sensor network. For some applications, it is additionally imperative to admit the fault signal to the collected data. To monitor the industrial environment through a wireless sensor network (WSNs), present a neural network based Levenberg-Marquardt (LM) Algorithm for detecting the fault using the gradient value and mean square error of the signal. The data are collected and presented by the magnetic flux sensor and MEMS acoustic sensor. The simulation model is developed in MATLAB/Simulink.

Permanent magnet synchronous motor is a robust machine for electrical vehicle application which can provide maximum torque at starting with low power and control of such machine is complex. This paper presents a model based predictive methodology for current control for PMSM drive powered through hybrid battery and fuel cell sourced electrical vehicle. Advantage of a MPC controller is it can predict the future changes in the system based on past and present inputs and enhances the dynamic performance of the PMSM control system. MPC controller increases the efficiency of the proposed system by ensuring precise control over output current from the drive. A buck-boost converter is employed to provide optimum dc link performance required for the voltage source inverter fed PMSM motor. The main objective of the buck boost converter is to boost the voltage available from battery and fuel cell even during low state of charge regions for stable operation of the drive. A suitable electrical vehicle model was developed in MATLAB/Simulink environment to validate the advantages of proposed conversion and control system for PMSM drive.

No abstract