MATLAB Code Implementation of Fuzzy MPPT Control

Implementation of Fuzzy MPPT Control in Photovoltaic Systems

Maximum Power Point Tracking (MPPT) is a critical technology for improving energy conversion efficiency in photovoltaic systems. Fuzzy logic control has become a popular MPPT solution due to its adaptability to nonlinear systems. Below is a MATLAB-based implementation approach for fuzzy MPPT control, consisting of three main modules: photovoltaic cell modeling, Boost circuit simulation, and fuzzy controller design.

Photovoltaic Cell Module The mathematical model of photovoltaic cells is implemented using MATLAB functions or Simulink blocks, primarily considering the effects of light intensity, ambient temperature, and cell parameters (such as series resistance and reverse saturation current). The output characteristic curves (I-V and P-V curves) are used to validate the nonlinear characteristics of the model under different conditions. Implementation Tip: Use MATLAB's "pvArray" function from the Simulink Simscape Electrical library or create custom equations with the Shockley diode model to simulate cell behavior.

Boost Circuit Module The Boost circuit is used for voltage boosting and maximum power transfer. By adjusting the duty cycle of the switching transistor, inductor current and output voltage are controlled. MATLAB's SimPowerSystems toolbox can be used to build the circuit topology, combined with a PWM generator to simulate actual hardware control. Code Insight: Implement the circuit using "simscape" components with controlled voltage sources and switching devices, where duty cycle modulation can be programmed via MATLAB scripts or Simulink PID controllers.

Fuzzy Controller Design The fuzzy logic controller uses the power change (dP) and voltage change (dV) of the photovoltaic cell as inputs, outputting the duty cycle adjustment (ΔD). Specific steps include: Fuzzification: Convert input and output quantities into fuzzy linguistic variables (e.g., "Negative Large", "Positive Small"). Rule Base: Establish inference rules such as "If dP is positive and dV is negative, then increase the duty cycle." Defuzzification: Convert fuzzy output into precise duty cycle signals using methods like the center of gravity. Algorithm Note: Design membership functions with "fuzzyLogicDesigner" toolbox and implement rule evaluation using Mamdani-type inference for smooth control action.

This solution enables modular integration and testing through Simulink, demonstrating rapid convergence to the maximum power point even under sudden light changes, thereby validating the robustness of fuzzy control. Simulation Tip: Use "Scope" blocks to monitor power waveforms and "To Workspace" blocks to analyze efficiency metrics via post-processing scripts.