Error in flood_algorithm_rtc_pv (line 22) best_fitness = objective_function(best_solution, V_data, I_data); i don not understand this error

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Haidy 2025-1-29

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https://ww2.mathworks.cn/matlabcentral/answers/2173443-error-in-flood_algorithm_rtc_pv-line-22-best_fitness-objective_function-best_solution-v_dat

移动：Torsten 2025-1-29

flood_algorithm_rtc_pv()
Arrays have incompatible sizes for this operation.

Error in solution>@(V)Iph-I0*(exp((V+Rs*I_actual)/(n*Vt))-1)-(V+Rs*I_actual)/Rsh (line 57)
I_model = @(V) Iph - I0 * (exp((V + Rs * I_actual) / (n * Vt)) - 1) - (V + Rs * I_actual) / Rsh;

Error in solution>objective_function (line 58)
I_calc = I_model(V);%arrayfun(I_model, V);

Error in solution>flood_algorithm_rtc_pv (line 23)
best_fitness = objective_function(best_solution, V_data, I_data);
function flood_algorithm_rtc_pv
% RTC Silicon Solar Cell Data (example data, replace with datasheet values)
V_data = [0:0.1:0.6]; % Voltage (V), replace with actual data
I_data = [0.7605, 0.754, 0.746, 0.734, 0.717, 0.693, 0.661, ...
    0.620, 0.570, 0.510, 0.450, 0.390, 0.330, 0.260, ...
    0.180, 0.090, 0.010, 0]; % Example current points (adjust if real data available)
% Flood Algorithm Parameters
population_size = 50; % Number of water drops
max_iterations = 500; % Maximum iterations
evaporation_rate = 0.2; % Water loss per iteration
spread_factor = 0.05; % Spread factor (smaller for fine-tuning)
bounds = [0.02, 0.03; % Iph bounds (close to Isc from datasheet)
    1e-9, 1e-6; % I0 bounds
    0, 0.5; % Rs bounds
    100, 1000; % Rsh bounds
    1, 2]; % n bounds (ideality factor)
% Initialize water drops (population)
population = initialize_population(population_size, bounds);
best_solution = population(1, :);
best_fitness = objective_function(best_solution, V_data, I_data);
% Flood Algorithm Loop
for iter = 1:max_iterations
    for i = 1:population_size
        % Evaluate fitness of each drop
        fitness = objective_function(population(i, :), V_data, I_data);
        if fitness < best_fitness
            best_fitness = fitness;
            best_solution = population(i, :);
        end
        
        % Evaporation and Spread
        population(i, :) = evaporate_and_spread(population(i, :), bounds, spread_factor, evaporation_rate);
    end
    
    % Display progress
    if mod(iter, 50) == 0
        fprintf('Iteration: %d, Best Fitness: %.6f\n', iter, best_fitness);
    end
end
% Results
fprintf('Optimal Parameters:\n');
fprintf('Iph: %.6f, I0: %.6e, Rs: %.6f, Rsh: %.6f, n: %.6f\n', best_solution);
end
% Objective Function: Root Mean Square Error (RMSE)
function error = objective_function(params, V, I_actual)
Iph = params(1);
I0 = params(2);
Rs = params(3);
Rsh = params(4);
n = params(5);
Vt = 0.025; % Thermal voltage at 25°C (adjust if needed)
I_model = @(V) Iph - I0 * (exp((V + Rs * I_actual) / (n * Vt)) - 1) - (V + Rs * I_actual) / Rsh;
I_calc = I_model(V);%arrayfun(I_model, V);
error = sqrt(mean((I_actual - I_calc).^2));
end
% Initialize Population
function population = initialize_population(population_size, bounds)
num_params = size(bounds, 1);
population = zeros(population_size, num_params);
for i = 1:num_params
    population(:, i) = bounds(i, 1) + (bounds(i, 2) - bounds(i, 1)) * rand(population_size, 1);
end
end
% Evaporate and Spread
function new_drop = evaporate_and_spread(drop, bounds, spread_factor, evaporation_rate)
num_params = length(drop);
new_drop = drop;
for i = 1:num_params
    % Evaporation (reduce by evaporation rate)
    new_drop(i) = new_drop(i) * (1 - evaporation_rate);
    
    % Spread (random perturbation within bounds)
    spread = spread_factor * (bounds(i, 2) - bounds(i, 1));
    new_drop(i) = new_drop(i) + spread * (2 * rand - 1);
    
    % Ensure bounds are respected
    new_drop(i) = max(min(new_drop(i), bounds(i, 2)), bounds(i, 1));
end
end