top of page
Search

MATLAB Implementation of 100 kW Grid Connected PV System with Fuzzy and P&O MPPT

We explore a MATLAB/Simulink simulation of a 100 kW grid-connected photovoltaic (PV) system. The key focus is on comparing two Maximum Power Point Tracking (MPPT) techniques: Fuzzy Logic Control (FLC) and the Perturb and Observe (P&O) method. The goal is to determine which approach provides better efficiency and stability under varying solar irradiance and temperature conditions.

System Configuration

The PV array consists of solar panels rated at 330 W each. These are arranged in 5 panels per series string, with a total of 66 parallel strings. The maximum theoretical output of the system is around 100.7 kW under standard test conditions of 1000 W/m² irradiance.

The system feeds power through a boost converter and a three-level inverter, eventually stepping up the voltage via a transformer (from 260 V to 25 kV) for grid connection.



Fuzzy Logic MPPT Controller (FLC)

The Fuzzy MPPT controller works by:

  1. Receiving inputs from the PV array: voltage and current.

  2. Calculating instantaneous power and the difference in power between successive time steps (ΔP).

  3. Calculating the change in current (ΔI) and deriving the voltage variation (ΔV = ΔP/ΔI).

  4. Defining two fuzzy inputs:

    • Error (ΔV)

    • Change in error (ΔV - previous ΔV)

  5. Using a 49-rule fuzzy inference system with seven membership functions each for inputs and output (duty cycle).

  6. Generating a duty cycle signal to control the boost converter.

The fuzzy controller provides a more adaptive approach to MPPT by adjusting rapidly and accurately to fluctuations in environmental conditions.



P&O MPPT Controller

The Perturb and Observe (P&O) method uses a more traditional logic-based algorithm:

  1. It monitors the change in power (ΔP) and change in voltage (ΔV).

  2. If both ΔP and ΔV have the same sign (positive or negative), it decreases the duty cycle.

  3. If ΔP and ΔV have different signs, it increases the duty cycle.

This method is simpler to implement but can be prone to oscillations around the maximum power point, especially under rapidly changing irradiance.

Inverter and Grid Integration

After MPPT, the system uses:

  • PI Controllers for voltage regulation.

  • Park and inverse Park transformations for converting signals between ABC and DQ frames.

  • Feedforward decoupling control to manage active and reactive power.

  • A three-level PWM generator to switch the inverter, converting DC to AC for grid injection.

Simulation Conditions

The simulation tests the system under different environmental conditions:

  • Irradiance changes from 1000 W/m² to 250 W/m², then back to 1000 W/m².

  • Temperature increases from 25°C to 50°C after 2 seconds.

These variations help evaluate the performance and robustness of each MPPT method.

Performance Comparison

Power Output

  • Fuzzy MPPT reaches peak power faster and maintains a stable power output.

  • P&O MPPT struggles with slower response times and larger fluctuations in duty cycle.

Duty Cycle Variation

  • The fuzzy method keeps the duty cycle relatively steady.

  • P&O shows frequent changes in the duty cycle, indicating less consistent tracking.

Grid Power Delivery

  • Fuzzy MPPT results in higher power injection into the grid.

  • P&O MPPT delivers less power, particularly under dynamic irradiance and temperature changes.

Conclusion

The simulation results clearly show that Fuzzy Logic MPPT outperforms the Perturb and Observe method in:

  • Efficiency of maximum power extraction,

  • Stability under fluctuating environmental conditions,

  • Faster convergence to the Maximum Power Point (MPP), and

  • Better overall grid power delivery.

Hozzászólások

bottom of page