PSO Trained ANFIS MPPT for Solar PV system
Introduction
We will explore the integration of PSO with ANFIS to improve solar PV system performance. ANFIS is a hybrid system that combines neural networks and fuzzy logic principles to model complex systems. By using PSO to fine-tune ANFIS parameters, we aim to achieve efficient Maximum Power Point Tracking (MPPT) for solar panels.
System Overview
Our system comprises several layers, with specific roles:
Layer 1: Distributes inputs using membership functions.
Layer 4: Performs calculations based on the formula f1=p1x1+q1y+r1f_1 = p_1 x_1 + q_1 y + r_1f1=p1x1+q1y+r1.
Layers 1 and 4 are tunable, with parameters like the Gaussian membership function's center and standard deviation being adjustable. In Layer 4, parameters p1p_1p1, q1q_1q1, and r1r_1r1 are also tunable.
The Role of Particle Swarm Optimization (PSO)
Traditionally, parameters in ANFIS are tuned using backpropagation and least squares methods. In our approach, we use PSO to optimize these parameters, enhancing the system’s performance. PSO is a computational technique inspired by the social behavior of birds flocking or fish schooling, which effectively searches for optimal solutions.
Simulation Setup
Data Generation
The simulation begins with generating data using a MATLAB program. This program simulates solar PV systems, with inputs including temperature and irradiation. The output is the voltage at the maximum power point (VMP). We collect thousands of samples to train our ANFIS model.
ANFIS Training with PSO
The training process involves the following steps:
Load Data: Use MATLAB commands to load and prepare the data.
Initial Fuzzy System Creation: Generate the initial fuzzy inference system using fuzzy c-means clustering.
Training with PSO: Utilize PSO to optimize the ANFIS parameters, minimizing the Root Mean Square Error (RMSE) between the predicted and actual outputs.
Detailed Steps in MATLAB
Creating Initial Membership Functions
We use fuzzy c-means clustering to generate the initial fuzzy inference system. This involves separating input and target data and preparing it for training.
Training Program
The training program integrates PSO to tune parameters. This involves setting up the PSO algorithm with parameters such as:
Number of variables
Population size
Number of iterations
Learning coefficients
The objective function in this setup is to minimize the RMSE, calculated as the difference between the predicted output and the target.
Simulation and Results
After training, we simulate the model in MATLAB's Simulink environment:
PV Panel Configuration: Includes specifications for a 250-watt panel.
Load and Boost Converter: Connects the panel to the load through a boost converter controlled by the optimized ANFIS model.
Simulation Results:
Performance: The PSO-trained ANFIS model effectively extracts maximum power across various irradiation conditions.
Load Variations: The model adapts to changes in load and maintains optimal power extraction.
Conclusion
Our MATLAB simulation demonstrates the effectiveness of using PSO to enhance ANFIS for solar PV systems. This approach not only improves power extraction but also adapts to varying environmental conditions and load changes.
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