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MATLAB Implementation of Fuzzy MPPT for Solar PV Battery DC Microgrid System

Overview of the System

The simulation model consists of a 2,000 W solar PV panel connected to a DC bus via a boost converter. In this configuration, the PV panel generates power that is either used by the system or stored in a battery bank. The battery consists of 20 12V batteries connected in series to provide a total 48V rated voltage.

This system uses the Fuzzy MPPT algorithm to control the boost converter and extract the maximum power from the solar PV panel, despite fluctuations in irradiation and temperature. The output of the PV system depends heavily on these environmental factors, which is why using an MPPT algorithm is essential.

Why MPPT is Important

Solar panels exhibit nonlinear behavior in their power output, meaning that the power produced by the PV panel can vary dramatically with changes in irradiation and temperature. To ensure maximum energy extraction, it's crucial to operate the panel at its optimal maximum power point (MPP).

Without MPPT, the panel could be running at less than its optimal power point, wasting potential energy. Therefore, the Fuzzy MPPT algorithm continuously adjusts the system to track the maximum power point, taking real-time measurements of voltage and current from the PV panel.

Fuzzy MPPT Algorithm

The core of this system’s functionality lies in the Fuzzy MPPT algorithm, which works by measuring the voltage and current from the PV panel. These two parameters are then used as inputs for the fuzzy logic controller.

The algorithm calculates:

  1. The change in voltage (ΔV\Delta VΔV)

  2. The change in power (ΔP\Delta PΔP)

These changes are input into the fuzzy logic system, which uses predefined rules to adjust the duty cycle of the boost converter. This adjustment allows the system to dynamically track the maximum power point by regulating the pulse signal sent to the IGBT (Insulated Gate Bipolar Transistor), which controls the boost converter.

Fuzzy Logic Controller and Membership Functions

The fuzzy logic controller uses a set of 49 rules based on two inputs:

  1. The error in voltage (the difference between the current and the previous voltage)

  2. The rate of change of error (the change in the error over time)

These inputs help the fuzzy controller generate the necessary duty cycle, which controls the boost converter and maximizes the energy output from the solar panel.

The system also incorporates membership functions to map the inputs (error and slope) to a set of fuzzy values. These functions help the system make decisions that are more in line with real-world conditions, ensuring smoother operation and more efficient energy extraction.

Power Management and Battery Control

One of the critical features of the system is its ability to manage power balance between the solar PV array, the battery, and the DC load.

The DC bus voltage is maintained at a constant value of 400V. The system compares the actual DC bus voltage with a reference voltage to determine if the system is balanced. If the solar power is greater than the load power, the excess energy is used to charge the battery. If the solar power is equal to the load power, the battery remains idle. However, when the solar power is insufficient to meet the load power, the system draws energy from the battery.

This ensures that the system always has a stable power supply while maximizing the use of solar energy and minimizing battery discharges.

Simulation and Results

In the simulation model, the irradiation levels are varied in intervals to observe the system’s response. The radiation intensity is adjusted every 3 seconds to test the system’s ability to maintain maximum power point tracking and power balance under changing environmental conditions.

For example:

  • When irradiation is high (1,000 W/m²), the system generates 2,000 W of power, and the battery enters charging mode as excess power is available.

  • As the irradiation decreases (e.g., 800 W/m², 500 W/m²), the system’s power output drops. If the power generated is insufficient to meet the load requirement, the battery discharges to provide additional power.

This simulation demonstrates how the system efficiently manages power between the solar PV array, the battery, and the load, ensuring that the DC bus voltage remains constant and energy is optimally utilized.

Benefits of Fuzzy MPPT in Solar PV Systems

The integration of Fuzzy MPPT into a solar PV system offers several key benefits:

  1. Optimal Power Extraction: The Fuzzy MPPT algorithm ensures that the solar panel always operates at its maximum power point, regardless of environmental conditions.

  2. Efficient Power Management: By continuously balancing power between the PV array, battery, and DC load, the system optimizes energy usage and minimizes wastage.

  3. Battery Longevity: The system intelligently charges and discharges the battery, ensuring it is used efficiently and remains in good condition over time.

  4. Stable DC Bus Voltage: The DC bus voltage is maintained at a constant level, ensuring a stable power supply for the load at all times.

Conclusion

The MATLAB implementation of Fuzzy MPPT for a solar PV battery DC microgrid system effectively maximizes energy extraction from the PV panels and maintains system stability. By using fuzzy logic to adjust the duty cycle of the boost converter and dynamically track the maximum power point, the system ensures efficient energy production and optimal power management.

This approach not only enhances the performance of the solar PV system but also contributes to the stability and reliability of off-grid or DC microgrid systems, making it a powerful solution for sustainable energy management.

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