Genetic Algorithm based on mppt for off-grid solar pv system write a code for me... i need help to integrate with the simulink parameters and reduce errors in code

function best_duty_cycle = testing1(~, I_pv )

% Prompt the user to enter the values of the input arguments

V_pv = 210

I_pv = 22

population_size = 2

max_generations = 60

% Initialize the population with random duty cycles

population = rand(population_size, 1);

% Initialize the best duty cycle

best_duty_cycle = population(1);

% Initialize the best fitness

best_fitness = fitness(best_duty_cycle, V_pv, I_pv);

% Loop through the generations

for generation = 1:max_generations

% Evaluate the fitness of the population

fitness_values = arrayfun(@(x) fitness(x, V_pv, I_pv), population);

% Select the individuals for reproduction

selected_indices = randsample(1:length(population), length(population), true, fitness_values);

% Perform crossover

for i = 1:2:length(population)

parent1 = population(selected_indices(i));

parent2 = population(selected_indices(i+1));

crossover_points = randi([1, length(parent1)], 1);

child1 = [parent1(1:crossover_points), parent2(crossover_points+1:end)];

child2 = [parent2(1:crossover_points), parent1(crossover_points+1:end)];

population([i, i+1]) = [child1, child2];

end

% Perform mutation

mutation_probabilities = rand(length(population), 1);

mutation_indices = find(mutation_probabilities < 0.01);

population(mutation_indices) = population(mutation_indices) + 0.1 * randn(length(mutation_indices), 1);

% Update the best duty cycle and fitness

current_best_index = find(fitness_values == max(fitness_values));

if fitness_values(current_best_index) > best_fitness

best_duty_cycle = population(current_best_index);

best_fitness = fitness_values(current_best_index);

end

end

end

function fitness = fitness(best_duty_cycle, V_pv, I_pv)

% Calculate the power

power = best_duty_cycle .* V_pv .* I_pv;

% Calculate the fitness

fitness = sum(power);

end

Answer 1

[Simar]

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