Photovoltaic system(Pv) with fuzzy and anfis Mppt simulation model using MATLAB

Enhancing Photovoltaic Systems with MPPT Controllers Using MATLAB

Over the last few years, the urgent need for new energy alternatives has intensified due to the global energy crisis and climate change. This has significantly driven the growth of renewable energies. Among these, photovoltaic systems stand out as a highly competitive solution. However, they are not without challenges, primarily due to the inefficient energy conversion. To address this issue, implementing a Maximum Power Point Tracking (MPPT) controller is essential to extract the maximum electrical power from photovoltaic modules under varying conditions.

Various MPPT Methods

Numerous MPPT methods have been developed and studied, including:

• Perturb and Observe (P&O)
• Fractional open-circuit voltage
• Fractional short-circuit current
• Incremental conductance (IncCon)
• Line approximation
• Ripple correlation control (RCC)
• PID control
• Fuzzy logic control (FLC)
• Genetic algorithms
• Neural network and neuro-fuzzy approaches

Intelligent Systems and Their Advantages

Intelligent systems like fuzzy logic control, neural networks, and genetic algorithms have the ability to self-tune their parameters and operate effectively under highly nonlinear conditions. Recent advancements have seen the hybridization of these techniques, such as the Adaptive Network Fuzzy Inference System (ANFIS). ANFIS combines the strengths of fuzzy logic and neural networks, providing a powerful method for handling imprecise data and incorporating learning capabilities.

FLC-Based MPPT Algorithms

FLC-based MPPT algorithms are increasingly attracting researchers due to their robust performance. Compared to traditional methods like the P&O algorithm, FLC-based techniques offer superior tracking performance. Recent literature highlights various MPPT techniques leveraging intelligent systems, demonstrating their effectiveness in optimizing photovoltaic systems.

ANFIS (Neuro-Fuzzy) MPPT Controller

ANFIS integrates fuzzy logic (FL) with artificial neural networks (ANN) to create a hybrid learning technique. Each layer of the ANN in ANFIS performs a function of FL:

• Layer 1: Input layer
• Layer 2: Membership function
• Layer 3: Rule base
• Layer 4: Output sum of the third layer nodes
• Layer 5: Output layer

The ANFIS toolbox in MATLAB constructs a fuzzy inference system (FIS) where membership function parameters are adjusted using a back-propagation algorithm or a hybrid approach combining back-propagation and the least squares method. This hybrid learning technique allows fuzzy systems to learn from data, enhancing their ability to perform nonlinear mapping, modeling, pattern recognition, and learning.

In conclusion, integrating MPPT controllers, especially those based on intelligent systems like ANFIS, significantly enhances the efficiency and robustness of photovoltaic systems. As renewable energy continues to grow, advanced methods like ANFIS are pivotal in overcoming challenges and optimizing performance. MATLAB provides a versatile platform for designing and implementing these sophisticated control algorithms, driving forward the capabilities of renewable energy technologies.