Intelligent algorithm-based maximum power point tracker for an off-grid photovoltaic-powered direct-current irrigation system

This research aims to enhance the performance of photovoltaic(PV)systems on a 2-fold basis.Firstly,it introduces an advanced deep artificial neural network algorithm for accurate and fast maximum power point tracking,ensuring optimal extraction of electrical power from PV arrays.Secondly,it proposes the use of 96-V,2.98-kW direct-current(DC)water pumps for farm irrigation,aiming to improve efficiency,reduce cost and complexity,and overcome challenges associated with connecting faraway farm irrigation systems to the utility grid.In this study,it has been demonstrated that the use of DC pumps greatly improves system performance and efficiency by eliminating the need for isolation transformers,power passive filters and inverters,therefore simplifying the architecture of the system.The efficacy of the proposed methodology is confirmed by MATLAB®/Simulink®simulation results,whereby the proposed algorithm attains a mean squared error of 6.5705×10^(-5)and a system efficiency approaching 99.8%,ensuring a steady voltage under varying load conditions.

Efficient maximum power point tracker based on neural network and sliding-mode control for buck converters

This paper presents detailed design steps of an effective control system aiming to increase the solar energy harvested via photovoltaic power-generation systems.The design of an intelligent maximum power point tracker(MPPT)supported by a robust sliding-mode(SM)controller is discussed in this study.The proposed control scheme is designed to track the MPP and provide a smooth system response by removing the overshoot in the load current during any variation in the connected load.Such a system is suitable for DC-DC buck converter applications.The study begins with modelling the buck converter for a continuous current mode operation.The reference voltage of the tracking system is produced by the proposed neural network(NN)algorithm.The proposed intelligent MPPT integrated with an SM controller is simulated in a MATLAB®/Simulink®platform.The simulation results are analysed to investigate and confirm the satisfaction level of the adopted four-serially connected PV-modules system.The system performance is evaluated at a light intensity of 500 W/m^(2) and an ambient temperature of 25°C.Applying only the proposed NN algorithm guarantees the MPP tracking response by delivering 100 W at a resistive load of 13Ω,and 200 W at a load of 6.5Ω,respectively,with 99.77%system efficiency.However,this simultaneously demonstrates a current spike of~0.5 A when the load is varied from 50%to 100%.The integrated SM controller demonstrates a robust and smooth response,eliminating the existing current spike.

Experimental investigation of photovoltaic systems for performance improvement using water cooling

This research aims to analyse the comparative performance of two identical photovoltaic(PV)panels with load variations and integrating an automated water-cooling process under the climatic conditions of the United Arab Emirates.The work also presents the steps of system design,implementation and performance evaluation of the proposed PV system,and all electrical,control and mechanical components along with how they were integrated within a 100-W PV system.MATLAB/Simulink?was used only to simulate the behaviours of the PV panel under wide ranges of incident sunlight and ambient temperature.The tests were performed for a day-long operation during a clear summer day.The experimental results demonstrate an improvement in the PV system performance compared with the uncooled system by~1.6%in terms of total harvested energy using the proposed water-cooling process with a frequency of 2 minutes of cooling operation every 30 minutes during day hours.