Re: Wind turbines

[amir ali Hosseini]

hi
may be help to you this attachment.
beast regards!

On Thu, Nov 22, 2018 at 1:54 AM Αμαλία Μαυρογιάννη <amalia...@gmail.com>
wrote:

> Hello,
> I have a system with 4 wind turbines. How can I simulate the system? So,
> can I add this data in my casefile or I have to use addwind? If I can add
> the wind turbines in my case how can I?
>
> Kind regards!
> Amalia Mavrogianni
>

clc;
clear;
close all;
%% Problem Definition
% Cost Function
CostFunction = @(x) CASE30_COST(x);
%nVar=90;             % Number of Decision Variables
nVar=60;             % Number of Decision Variables
VarSize=[1 nVar];   % Decision Variables Matrix Size
VarMin=0;         % Decision Variables Lower Bound
VarMax= 4;         % Decision Variables Upper Bound
%% TLBO Parameters
MaxIt =5;        % Maximum Number of Iterations
nPop = 60;          % Population Size
%% Initialization 
% Empty Structure for Individuals
empty_individual.Position = [];
empty_individual.Cost = [];
% Initialize Population Array
pop = repmat(empty_individual, nPop, 1);
% Initialize Best Solution
BestSol.Cost = inf;
% Initialize Population Members
for i=1:nPop
    
    %pop(i).Position =[randsample([0 1 2 
3],30,1),unifrnd(0,1,1,30),randsample([1.2 2 3],30,1)]';
    pop(i).Position =[randsample([0 1 2 3],30,1),randsample([1.2 2 3],30,1)]';
    pop(i).Cost = CostFunction(pop(i).Position);
    
    if pop(i).Cost < BestSol.Cost
        BestSol = pop(i);
    end
end

% Initialize Best Cost Record
BestCosts = zeros(MaxIt,1);

%% TLBO Main Loop

for it=1:MaxIt
    % Calculate Population Mean
    Mean = 0;
    for i=1:nPop
        Mean = Mean + pop(i).Position;
    end
    Mean = Mean/nPop;% average of class
    
    % Select Teacher
    Teacher = pop(1);
    for i=2:nPop
        if pop(i).Cost < Teacher.Cost
            Teacher = pop(i);
        end
    end
    
    % Teacher Phase
    for i=1:nPop
        % Create Empty Solution
        newsol = empty_individual;
        
        % Teaching Factor
        TF = randi([1 2]);
        
        % Teaching (moving towards teacher)
        newsol.Position = pop(i).Position ...
            + (rand(VarSize))'.*(Teacher.Position - TF*Mean);
        
        % Clipping
        newsol.Position = max(newsol.Position, VarMin);
        newsol.Position = min(newsol.Position, VarMax);
        
        % Evaluation
        newsol.Cost = CostFunction(newsol.Position);
        
        % Comparision
        if newsol.Cost<pop(i).Cost
            pop(i) = newsol;
            if pop(i).Cost < BestSol.Cost
                BestSol = pop(i);
            end
        end
    end
    
    % Learner Phase
    for i=1:nPop
        
        A = 1:nPop;
        A(i)=[];
        j = A(randi(nPop-1));
        
        Step = pop(i).Position - pop(j).Position;
        if pop(j).Cost < pop(i).Cost
            Step = -Step;
        end
        
        % Create Empty Solution
        newsol = empty_individual;
        
        % Teaching (moving towards teacher)
        newsol.Position = pop(i).Position + (rand(VarSize))'.*Step;%random 
"Varsize"  transposed multipel Step

 
        
        % Clipping
        newsol.Position = max(newsol.Position, VarMin);
        newsol.Position = min(newsol.Position, VarMax);
        
        % Evaluation
        newsol.Cost = CostFunction(newsol.Position);
        
        % Comparision
        if newsol.Cost<pop(i).Cost
            pop(i) = newsol;
            if pop(i).Cost < BestSol.Cost
                BestSol = pop(i);
            end
        end
    end
    
    % Store Record for Current Iteration
    BestCosts(it) = BestSol.Cost;
    
    % Show Iteration Information
    disp(['Iteration ' num2str(it) ': Best Cost = ' num2str(BestCosts(it))]);
    
end

%% Results

figure;
plot(BestCosts, 'LineWidth', 2);
%semilogy(BestCosts, 'LineWidth', 2);
xlabel('Iteration');
ylabel('Best Cost');
grid on;

save

matlab.mat
Description: Binary data