GA Trained ANFIS MPPT for Solar PV system

Genetic Algorithm (GA) trained Adaptive Neuro-Fuzzy Inference System (ANFIS) for Maximum Power Point Tracking (MPPT) of a Solar PV system

GA-Trained ANFIS MPPT Process:

• Objective Function: Define a fitness function that quantifies how well a given set of ANFIS parameters lead to MPPT. Typically, the objective is to maximize the power extracted from the solar PV system.
• GA Setup: Configure the GA parameters, such as the number of generations, population size, and mutation/crossover rates.
• Initial Population: Generate an initial population of ANFIS parameter sets (fuzzy logic membership functions, neural network weights, etc.).
• Evaluation: For each parameter set in the population, simulate the PV system's performance using ANFIS-based MPPT. Evaluate the power output and calculate the fitness based on how close it is to the MPP.
• Selection: Choose the best-performing parameter sets (individuals) based on their fitness to serve as parents for the next generation.
• Crossover and Mutation: Combine the selected parents to create new parameter sets, introducing diversity through genetic operations like crossover (mixing parameters of parents) and mutation (small random changes).
• Next Generation: Repeat the evaluation, selection, crossover, and mutation steps for multiple generations, gradually improving the parameter sets' fitness.
• Convergence: The GA converges towards parameter sets that provide optimal or near-optimal MPPT performance.

For more information : www.pirc.co.in

WhatsApp: http://lnkiy.in/pirc-whatsapp