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Fuzzy MPPT For Solar PV Battery DC Microgrid System in MATLAB

Fuzzy MPPT For Solar PV Battery DC Microgrid System in MATLAB

Welcome to LMS Solutions! Today, we'll delve into the application of Fuzzy MPPT for a Solar PV Battery DC Microgrid System. This simulation model encompasses a 2000 Watts PV panel connected to the DC bus via a boost converter, coupled with a 48V-rated battery connected via a buck-boost converter. The aim is to optimize power extraction from the PV panel by utilizing the Fuzzy Maximum Power Point Tracking (MPPT) algorithm.


The need for an MPPT algorithm arises due to the non-linear characteristics of solar panels dependent on irradiation and temperature. These changes affect the power output of the panel, making it crucial to extract maximum power by tracking the Maximum Power Point (MPP) under varying conditions.

The simulation model demonstrates the PV array characteristics, depicting the power-voltage (P-V) and current-voltage (I-V) curves. These curves showcase the varying peak points under different irradiation conditions, emphasizing the importance of accurately identifying the MPP for maximum power extraction.

The Fuzzy MPPT algorithm utilizes the measured PV array details such as voltage and current as input. It implements a Fuzzy Logic Controller to process the voltage and current inputs, computing the change in voltage and change in power to determine the optimal operating point for the PV panel.

The Fuzzy Logic System integrates multiple rules (49 in this case), considering variations in error and change in slope, to modulate the duty cycle of the boost converter. This adjustment enables the system to control the boost converter and extract maximum power from the solar PV system.

Additionally, in this microgrid system, the DC bus voltage is maintained at 400 volts. A Proportional-Integral (PI) controller manages the DC bus voltage, ensuring it remains constant by generating pulse-width modulation (PWM) signals for the voltage controller of the buck-boost converter.

The power balance within the system is crucial. The excess power from the PV panel is used to charge the battery when available, while the battery supplies power to the load when needed. The control mechanism enables the system to adapt to changing power demands, managing charging and discharging modes based on the PV power and load conditions.

The simulation involves testing the system under varying irradiation conditions (1000, 800, 500, 300 watts per square meter). Results demonstrate effective power balance management and the system's ability to adjust power flow between the PV panel, battery, and load based on changing conditions.

The simulation involves testing the system under varying irradiation conditions (1000, 800, 500, 300 watts per square meter). Results demonstrate effective power balance management and the system's ability to adjust power flow between the PV panel, battery, and load based on changing conditions.nt control mechanisms in place.

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