Hi Mohammed,
As per my understanding you want to find the best duty cycle that maximizes power output of solar PV system and require assistance in integrating this code with Simulink parameters to minimize errors.Current implementation has few areas that need attention for integration with Simulink and for the GA to function correctly. Here is a revised version of code:
function best_duty_cycle = testing1(V_pv, I_pv)
% Initialization
population_size = 10; % Consider increasing for better results
max_generations = 60;
mutation_rate = 0.01; % Mutation probability
crossover_rate = 0.8; % Proportion of population to undergo crossover
% Initialize the population with random duty cycles
population = rand(population_size, 1);
% Evaluate initial population fitness
fitness_values = arrayfun(@(duty_cycle) calculate_fitness(duty_cycle, V_pv, I_pv), population);
% Find the best initial duty cycle and fitness
[best_fitness, best_idx] = max(fitness_values);
best_duty_cycle = population(best_idx);
% Genetic Algorithm Loop
for generation = 1:max_generations
% Selection - Roulette Wheel Selection
selected_indices = roulette_wheel_selection(fitness_values);
% Crossover
offspring = crossover(population(selected_indices), crossover_rate);
% Mutation
offspring = mutation(offspring, mutation_rate);
% Ensure duty cycles are within [0, 1]
offspring = min(max(offspring, 0), 1);
% Evaluate fitness of the new population
fitness_values = arrayfun(@(duty_cycle) calculate_fitness(duty_cycle, V_pv, I_pv), offspring);
% Update the best duty cycle and fitness
[current_best_fitness, current_best_idx] = max(fitness_values);
if current_best_fitness > best_fitness
best_fitness = current_best_fitness;
best_duty_cycle = offspring(current_best_idx);
end
% Update the population
population = offspring;
end
end
function fitness = calculate_fitness(duty_cycle, V_pv, I_pv)
% Power calculation as the fitness function
power = duty_cycle * V_pv * I_pv;
fitness = power; % Objective is to maximize power
end
function selected_indices = roulette_wheel_selection(fitness_values)
cumulative_sum = cumsum(fitness_values);
total_sum = sum(fitness_values);
selected_indices = arrayfun(@(x) find(rand * total_sum <= cumulative_sum, 1), 1:length(fitness_values));
end
function offspring = crossover(population, crossover_rate)
offspring = population;
for i = 1:2:length(population)-1
if rand <= crossover_rate
crossover_point = randi([1, length(population(i))]);
offspring(i) = population(i) * crossover_point + population(i+1) * (1 - crossover_point);
offspring(i+1) = population(i+1) * crossover_point + population(i) * (1 - crossover_point);
end
end
end
function mutated_population = mutation(population, mutation_rate)
mutations = rand(size(population)) < mutation_rate;
mutation_amount = 0.1 * randn(sum(mutations), 1); % Adjust mutation size
mutated_population = population;
mutated_population(mutations) = population(mutations) + mutation_amount;
end
Key Changes:
- Increased Population Size: Larger population size can explore solution space more effectively.
- Proper Mutation and Crossover Functions: These are crucial for the diversity and convergence of the GA.
- Roulette Wheel Selection: Implements a selection mechanism based on fitness proportionate selection.
- Fitness Function: Represents the power output, which the MPPT aims to maximize.
- Mutation Rate and Crossover Rate: Controls the extent of mutation and crossover in the population.
- Bounds Checking: Ensures duty cycle values remain within valid bounds after mutation.
Integrating with Simulink:
- MATLAB Function Block: Use this block to integrate the MATLAB code with your Simulink model. Input V_pv and I_pv from your model into this block and output the best_duty_cycle to control your system. MATLAB Function Block- https://www.mathworks.com/help/simulink/ug/what-is-a-matlab-function-block.html?searchHighlight=MATLAB%20FUNCTION%20BLOCK&s_tid=srchtitle_support_results_1_MATLAB%20FUNCTION%20BLOCK
- Simulation Parameters: Ensure the simulation time step and parameters in Simulink match the expected conditions for the GA operation.
This approach should reduce errors and improve integration with Simulink for off-grid solar PV system's MPPT.
Hope it helps!
Best Regards,
Simar
