Alice Hepzibah A

This paper deals with the utilization of solar PV array to drive the Brushless DC motor for tiller machine application. Solar PV array maximum power extracted using DC-DC converter and maximum power point tracking algorithm. Simple perturb and observe method is employed to achieve the maximum efficiency of the solar PV array. The reason that Brushless dc motor has been chosen for the project is that these motors are cheaper and more robust than the more conventional DC motor. The tiller machine used for small land cultivation and compared to diesel tiller machine or tractor which are low maintenance, long life and more efficient. The renewable energy such that the solar power used to drive the BLDC motor. The simulation and hardware of solar fed BLDC motor has been carried out.

In this paper, adaptive neuro-fuzzy inference system and proportional integral controller-based maximum power point tracking algorithm are presented for solar powered brushless DC motor for water pumping application. Adaptive neuro-fuzzy inference with PI controller provides control gain to maximum power point tracker. It adjusts the duty cycle of the zeta converter for extracting maximum power from solar PV array. The performance of proposed controller is compared with the conventional perturb and observe method, fuzzy perturb and observe method and incremental conductance method. Simulation studies are carried out in MATLAB. The experimental verification is shown to prove the suitability and feasibility of the proposed controller. The results reveal that the adaptive fuzzy inference system with PI controller quickly tracks maximum power from solar PV array under different irradiance.

In this paper, adaptive neuro-fuzzy inference system and proportional integral controller-based maximum power point tracking algorithm are presented for solar powered brushless DC motor for water pumping application. Adaptive neuro-fuzzy inference with PI controller provides control gain to maximum power point tracker. It adjusts the duty cycle of the zeta converter for extracting maximum power from solar PV array. The performance of proposed controller is compared with the conventional perturb and observe method, fuzzy perturb and observe method and incremental conductance method. Simulation studies are carried out in MATLAB. The experimental verification is shown to prove the suitability and feasibility of the proposed controller. The results reveal that the adaptive fuzzy inference system with PI controller quickly tracks maximum power from solar PV array under different irradiance.

Fuel cells are a family of technologies that generate electricity through electrochemical process rather than combustion. Unlike a battery, a fuel cell will continuously produce electricity as along as fuel is supplied and the catalyst remains active. Fuel cells provide major environmental, energy and economic benefits that advance critical national goals. Fuel cell generates electricity by an electrochemical reaction in which oxygen and a hydrogen rich fuel combines to form water. Fuel cell types are generally classified according to the nature of the electrolyte they use. This paper deals with the modeling of PEMFC which uses water-based, acidic polymer membrane as its electrolyte, with platinum-based electrodes. Due to relatively low temperatures and the use of precious metal based electrodes, this cell must operate on pure hydrogen. PEMFC are currently the leading technology for light duty vehicles and material handling vehicles. To reduce the current and voltage ripples, a simu...