Fuzzy Maximum Power Point Tracking (MPPT) controller for photovoltaic system on mini greenhouse

Abstract

Greenhouse is currently used is urban farming. Smart greenhouse technology allows farmers to monitor their plants easily. This system requires much electrical energy supply. In order to save energy, additional electricity supply is needed. For this reason, photovoltaic is installed on the roof of the greenhouse. The obstacle in the installation of photovoltaic cells is that the power produced is not optimal, due to the movement of the sun’s position. For this reason, Maximum Power Point Tracking (MPPT) technology is used. In this paper, fuzzy logic was used for MPPT based on I and V from PV. The aim is to find the maximum operating point of a photovoltaic module so that it can increase the output power ratio of photovoltaic modules in varying temperatures and radiation levels. Simulation is done using MATLAB, so that a ratio of output power ratios is generated with and without using an MPPT. By using the fuzzy MPPT controller, it produces optimum power, at P PV max = 250 W, P out max = 160 W, P PV max = 174.8 W, P out max = 125 W, P PV max = 98.97 W, P out max = 75 W. This power will be used to supply electricity for 2 15 W lamps, 12 volts for the mini greenhouse.

GreenLab, an IoT-Based Small-Scale Smart Greenhouse

“…The energy transfer efficiency for the magnetic resonance coupling method and its relationship with the transferred energy power are analyzed. Another paper proposes an innovative use of the uniform magnetic field in a large space to charge the sensors in the intelligent greenhouse wirelessly, leading to the avoidance of the disadvantages of cable charging [15].…”

“…If the photovoltaic system is connected to other resources (diesel or wind generators), then it is called a hybrid photovoltaic system. Regarding photovoltaic systems for IoT-based greenhouses, several solutions were proposed in the literature [15][16][17]. The last solution includes also a mobile application with integrated cloud services for monitoring the photovoltaic system status.…”

Enhancing Photovoltaic Panel Performance Through Artificial Neural Network and Maximum Power Point Tracking

“…By using different values for temperature (20,25,30,35, 40, 45)℃ and constant solar irradiance 1000 W/m 2 , Figure 19 shows the PV power by using ANN and P&O under different temperature values. ANN simply can assign the reference voltage according to the changes in the current and voltage which improve the system response for the sudden changes.…”

“…This combination is especially useful in dynamic or partly shaded settings where the MPP varies often, enabling energy production to be more efficient by adjusting to changing load circumstances and environmental variables. However, the effectiveness of such a system depends on the quality of the ANN model, the MPPT algorithm, and the specific characteristics of the PV panels and load [20].…”