Comparative Study of P&O and Fuzzy MPPT Controllers and Their Optimization Using PSO and GA to Improve Wind Energy System

Many academics have recently focused on wind energy installations. WECS (wind energy conversion system) is a renewable energy source that has seen significant development in recent years. Furthermore, compared to the use of power grid supply, the use of the WECS in the water pumping field is a cost-free option (economically). The purpose of this study is to demonstrate a wind-powered pumping mechanism. To obtain the best option, it considers and contrasts four distinct approaches. This research aims to improve the system\u27s performance and the quality of the generated power. The objective of the control of WECS with a permanent magnet synchronous generator (PMSG) is to carefully maximize power generation. Finally, this research employed the fuzzy logic control (FLC) and particle swarm optimization (PSO) algorithms improved using a genetic algorithm (GA). The proposed system\u27s performance was tested using the generated output voltage, current, and power waveforms, as well as the intermediate circuit voltage waveform and generator speed. The provided data show that the control technique used in this study was effective

Measurement and Simulation of 2.25 kWp grid-connected amorphous photovoltaic station in a hot desert environment

This work investigates measurements and simulations of a 2.25 kWp grid-connected amorphous photovoltaic power plant mounted on a parked car in a hot desert environment. This power station is located at applied research unit field (URAER), in the Ghardaia region, southern region. The simulation is carried out using PVSYS software. This includes evaluation of meteorological and electrical parameters performance of studied PV system such as reference PV system, PV array yield (YA), Final yield (YF), PV array and system losses, array and system efficiency, performance ratio (PR). The array nominal energy estimated at STC is 5695 kWh/year. The energy estimated injected into the grid is 4648 kWh/year

Comparative Study on Photovoltaic Pumping Systems Driven by Different Motors Optimized with Sliding Mode Control

This study investigates the performance of three different photovoltaic (PV) water pumping systems driven by three types of motors, namely a separately excited DC motor (DCM), an asynchronous motor (ASM), and a permanent magnet synchronous motor (PMSM), via a DC/DC buck-boost converter coupled to a centrifugal pump. The purpose of this study is to implement a fast and robust control for this type of a nonlinear system, controlled by sliding mode (SM). This paper presents an SM control technique for controlling a DC/DC buck-boost converter to transfer the maximum power delivered by the PV generator. Each component is studied and analyzed to simulate the global system in MATLAB/SIMULINK. The three systems are then compared to determine the overall effectiveness of the proposed command. The study concludes that the ASM-driven PV system yields highly favorable results and requires less maintenance compared with other systems

Comparative Study on Photovoltaic Pumping Systems Driven by Different Motors Optimized with Sliding Mode Control

This study investigates the performance of three different photovoltaic (PV) water pumping systems driven by three types of motors, namely a separately excited DC motor (DCM), an asynchronous motor (ASM), and a permanent magnet synchronous motor (PMSM), via a DC/DC buck-boost converter coupled to a centrifugal pump. The purpose of this study is to implement a fast and robust control for this type of a nonlinear system, controlled by sliding mode (SM). This paper presents an SM control technique for controlling a DC/DC buck-boost converter to transfer the maximum power delivered by the PV generator. Each component is studied and analyzed to simulate the global system in MATLAB/SIMULINK. The three systems are then compared to determine the overall effectiveness of the proposed command. The study concludes that the ASM-driven PV system yields highly favorable results and requires less maintenance compared with other systems

Adaptive Artificial intelligence based fuzzy logic MPPTcontrol for stande-alone photovoltaic system under different atmospheric conditions

<p><strong>there is an increased need for analysing the effect of atmospheric variables on photovoltaic (PV) production and performance. The outputs from the different PV cells in different atmospheric conditions, such as irradiation and temperature , differ from each other evidencing knowledge deficiency in PV systems [14]. Maximum power point tracking<em> </em>(MPPT)<em> </em>methods are used to maximize the PV array output power by tracking continuously the maximum power point (MPP). Among all MPPT methods existing in the literature, perturb and observe (P&amp;O) is the most commonly used for its simplicity and ease of implementation; however, it presents drawbacks such as slow response speed, oscillation around the MPP in steady state, and even tracking in wrong way under rapidly changing atmospheric conditions.</strong><strong> </strong><strong>In order to allow a functioning around the optimal point Mopt, we have inserted a <em>DC-DC </em>converter (Buck–Boost) for a better matching between the PV and the load</strong><strong>. This paper, we study the Maximum power point tracking using adaptive Intelligent fuzzy logic and conventional (P&amp;O) control for stande-alone photovoltaic Array system .In particular, the performances of the controllers are analyzed under variation weather conditions with are constant temperature and variable irradiation. The proposed system is simulated by using MATLAB-SIMULINK. According to the results, fuzzy logic controller has shown better performance during the optimization.</strong></p