2.4_Estimar_Parâmetros_Curva_IV_1Diodo_Jaya.ipynb

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   "source": [
    "# Estimando os Parâmetros Elétricos do Modelo de Um Diodo via Jaya\n",
    "\n",
    "Para tornar a convergência mais rápida, diminui o maxFes = 1000. Aumente se necessário! Roda em Octave ou Matlab."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "W4kU9XZw86yi"
   },
   "source": [
    "Para rodar online, use: https://mybinder.org/v2/gh/Calysto/octave_kernel/master?urlpath=/lab/tree/octave_kernel.ipynb"
   ]
  },
  {
   "cell_type": "code",
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   "source": [
    "%%octave\n",
    "\n",
    "function [Iph, I0, n, Rs, Rp, RMSE] = Jaya_PV_Original(Vmed, Imed, Ns, T, lim_inf, lim_sup, RUNS, pop, maxFes)\n",
    "%%\n",
    "% Descricao:\n",
    "%   Computa o algoritimo gentico Jaya de um diodo\n",
    "%\n",
    "% Entradas:\n",
    "%   Vmed - vetor de tensao medida [V]\n",
    "%   Imed - vetor de corrente medida [A]\n",
    "%   Ns   - numero de celulas\n",
    "%   T    - temperatura do mdulo  [C]\n",
    "%   lim_inf - vetor dos limites inferiores dos parametros a\n",
    "%      serem estimados. Formato: [Iph_min, I0_min, n_min, Rs_min, Rp_min];\n",
    "%   lim_sup - vetor dos limites superiores dos parametros a\n",
    "%      serem estimados. Formato: [Iph_max, I0_max, n_max, Rs_max, Rp_max];\n",
    "%   RUNS - quantidade de rodadas\n",
    "%   pop - tamanho da populacao de cada rodada\n",
    "%   maxFes - quantidade maxima de avaliacao da funcao objetivo\n",
    "%\n",
    "% Saidas:\n",
    "%   Iph - corrente fotogerada\n",
    "%   I0 - corrente de saturacao reversa\n",
    "%   n - indice de idealidade do diodo\n",
    "%   Rs - resistencia serie\n",
    "%   Rp - resistencia paralelo\n",
    "%   RMSE - erro quadradico medio\n",
    "\n",
    "%% Constantes\n",
    "k = 1.3806503e-23;    % Boltzmann [J/K]\n",
    "q = 1.60217646e-19;   % Electron charge [C]\n",
    "T = T + 273.15;       % Tempeture [K]\n",
    "Vt = Ns*k*T/q;        \n",
    "%%\n",
    "% Default values for RUNS, pop and maxFes.\n",
    "if isnan(RUNS)\n",
    "    RUNS = 10;  \n",
    "end\n",
    "if isnan(pop)\n",
    "    pop = 20;\n",
    "end\n",
    "if isnan(maxFes)\n",
    "    maxFes = 1000; % 50000 para melhores resultados\n",
    "end \n",
    "%% \n",
    "maxGen = floor(maxFes/pop); % quantidade maxima de geracoes\n",
    "%var = [Iph, I0, n, Rs, Rp];\n",
    "mini = lim_inf;       % limite inferior\n",
    "maxi = lim_sup;       % limite superior\n",
    "[~,var] = size(mini); % var  a quantidade de variaves.\n",
    "runs = 1;             % contador de rodadas    \n",
    "while(runs <= RUNS)\n",
    "    %% inicializacao da populacao\n",
    "    x = zeros(pop, var);\n",
    "    for i = 1:var\n",
    "        x(:,i) = mini(i)+(maxi(i)-mini(i))*rand(pop,1);\n",
    "    end\n",
    "    %%\n",
    "    gen = 1; % contador de geracoes\n",
    "    f = myobj(x, Vt, Imed, Vmed); % fitness inicial\n",
    "    while(gen <= maxGen)\n",
    "        xnew = updatepopulation(x, f);      % Cria nova populacao\n",
    "        xnew = trimr(mini,maxi,xnew);       % Manter variaveis dentro dos limites\n",
    "        fnew = myobj(xnew, Vt, Imed, Vmed); % Avalia fitness da nova polulacao\n",
    "\n",
    "        for i = 1:pop\n",
    "            if(fnew(i) < f(i))  % Os novos individuos possuem fitness melhor?\n",
    "                x(i,:) = xnew(i,:);\n",
    "                f(i) = fnew(i);\n",
    "            end\n",
    "        end\n",
    "        fopt(gen) = min(f); % Vetor que armazena o melhor fitness de cada gerao\n",
    "        gen = gen+1;\n",
    "    end\n",
    "    [fmin, id] = min(f);\n",
    "    xmelhores(runs,:) =  [x(id,:), fmin]; % Vetor que armazena o melhor de cada rodada\n",
    "%% Plotar graficos a cada fim de gerao\n",
    "%    disp('Parametros obtidos no ciclo i')\n",
    "%    Iph = x(id,1)\n",
    "%    I0 = x(id,2)\n",
    "%    n = x(id,3)\n",
    "%    Rs = x(id,4)\n",
    "%    Rp = x(id,5)    \n",
    "%    figure(runs)\n",
    "%    plot(Vmed, Imed,'o');\n",
    "%    hold on    \n",
    "%    fmodelo = @(V,I) (I - Iph*ones(size(I)) + I0*(exp((V+I*Rs)/(n*Vt))-1) + (V+I*Rs)/Rp);\n",
    "    % fimplicit(fmodelo,[0 Vmed(end) 0 Imed(1)]);\n",
    "%    ezplot(fmodelo,[0 20 0 7.8]); % Mudar\n",
    "   %temp = input('digite algo');\n",
    "%    xlim([0 Vmed(end)+1])\n",
    "%    ylim([0, Imed(1) + 0.2])\n",
    "%%    \n",
    "%     [val,ind] = min(fopt);   % \n",
    "%     Fes(runs) = pop*ind;     % number of function evaluations\n",
    "%     best(runs) = val;\n",
    "    runs = runs+1;\n",
    "end\n",
    "% valores medios dos parametros\n",
    "%disp('Parametros medios')    \n",
    "%Iph_med = mean(xmelhores(:,1))\n",
    "%I0_med = mean(xmelhores(:,2))\n",
    "%n_med = mean(xmelhores(:,3))\n",
    "%Rs_med = mean(xmelhores(:,4))\n",
    "%Rp_med = mean(xmelhores(:,5))\n",
    "%f_med = mean(xmelhores(:,6))\n",
    "\n",
    "%% Melhor resultado\n",
    "[temp, id] = min(xmelhores(:,6));\n",
    "Iph = xmelhores(id,1);\n",
    "I0 = xmelhores(id,2);\n",
    "n = xmelhores(id,3);\n",
    "Rs = xmelhores(id,4);\n",
    "Rp = xmelhores(id,5);\n",
    "RMSE = xmelhores(id,6);\n",
    "%disp('n Melhor resultado')\n",
    "%disp('[Iph, I0, n, Rs, Rp, fitness]');\n",
    "%disp(xmelhores(id,:))\n",
    "%fprintf('n Iph(A) = %f',xmelhores(id,1));\n",
    "%fprintf('n I0(uA) = %f',xmelhores(id,2)*10^6);\n",
    "%fprintf('n n = %f',xmelhores(id,3));\n",
    "%fprintf('n Rs(ohms) = %f',xmelhores(id,4));\n",
    "%fprintf('n Rph(ohms) = %f',xmelhores(id,5));\n",
    "%fprintf('n fmin = %f',xmelhores(id,6));\n",
    "\n",
    "\n",
    "\n",
    "%% Mostra resultados em termos de desvio padrao, media, minimo e max fitness\n",
    "%bbest = min(best);   \n",
    "%mbest = mean(best);  \n",
    "%wbest = max(best);   \n",
    "%stdbest = std(best);\n",
    "%mFes = mean(Fes);\n",
    "%fprintf('nn best = %f',bbest);\n",
    "%fprintf('n mean = %f',mbest);\n",
    "%fprintf('n worst = %f',wbest);\n",
    "%fprintf('n std. dev. = %f',stdbest);\n",
    "%fprintf('n mean Fes = %f',mFes);\n",
    "%fprintf('n');\n",
    "end\n",
    "\n",
    "%%\n",
    "function [z] = trimr(mini, maxi, x)\n",
    "[~,var] = size(x);\n",
    "for i = 1:var\n",
    "    x(x(:,i)<mini(i), i) = mini(i);\n",
    "    x(x(:,i)>maxi(i), i) = maxi(i);\n",
    "end\n",
    "z = x;\n",
    "end\n",
    "%% \n",
    "function [xnew] = updatepopulation(x, f)\n",
    "[pop, var] = size(x);\n",
    "[~, id_best] = min(f);\n",
    "[~, id_worst] = max(f);\n",
    "best = x(id_best,:);\n",
    "worst = x(id_worst,:);\n",
    "xnew = zeros(pop,var); % pre alocacao de memoria\n",
    "% for i = 1:pop\n",
    "%     for j = 1:var\n",
    "%         r = rand(1,2);\n",
    "%         xnew(i,j) = x(i,j) + r(1)*(best(j)-abs(x(i,j))) - r(2)*(worst(j)-abs(x(i,j)));\n",
    "%     end\n",
    "% end\n",
    "%% TESTAR ISSO AQUI!!!!!\n",
    "R1 = rand(pop,var);\n",
    "R2 = rand(pop,var);\n",
    "xnew = x + R1.*(best - abs(x)) - R2.*(worst - abs(x));\n",
    "\n",
    "end\n",
    "%%"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "S5dtK54786yw"
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   "source": [
    "Define-se a função objetivo, no caso o erro quadrático médio (RMSE) da corrente:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
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    "colab_type": "code",
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   "source": [
    "%%octave\n",
    "\n",
    "function [f] = myobj(x, Vt, Imed, Vmed)\n",
    "[pop,~] = size(x);\n",
    "RMSE = zeros(1,pop); % pre-alocacao de memoria\n",
    "for i = 1:pop\n",
    "    Iph = x(i,1);\n",
    "    I0 = x(i,2);\n",
    "    n = x(i,3);\n",
    "    Rs = x(i,4);\n",
    "    Rp = x(i,5);\n",
    "    RMSE(i) = sqrt(sum((Imed - Iph*ones(size(Imed)) + I0*(exp((Vmed+Imed*Rs)/(n*Vt))-1) + (Vmed+Imed*Rs)/Rp).^2)/length(Imed));\n",
    "end\n",
    "f = RMSE;\n",
    "end"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "JRd-yeHr86y8"
   },
   "source": [
    "## Tarefa 1\n",
    "\n",
    "Observe que para o cálculo do RMSE se utiliza o valor da corrente medida (Imed). Em si isso gera um pequeno erro, uma vez que o valor medido se difere do estimado. Estamos estudando uma forma de contornar isso. Dê e implemente uma sugestão."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "f7uygzCz86y_"
   },
   "source": [
    "# Chamando a função"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 568
    },
    "colab_type": "code",
    "id": "BtuqI8ms86zA",
    "outputId": "38adc83c-6856-46bf-b3f7-a398bb566435"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Parametros obtidos:\n",
      " Iph(A) = 1.028178\n",
      " I0(uA) = 5.201811\n",
      " n = 1.395238\n",
      " Rs(ohms) = 1.161067\n",
      " Rp(ohms) = 1949.018447\n",
      " RMSE = 0.002743 \n",
      "error: scalar cannot be indexed with .\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%octave\n",
    "warning('off')\n",
    "%% Dados de entrada\n",
    "%A = load('Dados_Photowatt-PWP 201.txt');\n",
    "% Vetor de tensoes medidas  [V]\n",
    "Vmed = [0.1248;1.80930;3.35110;4.76220;6.05380;7.23640;8.31890;9.30970;10.2163;11.0449;11.8018;12.4929;13.1231;13.6983;14.2221;14.6995;15.1346;15.5311;15.8929;16.2229;16.5241;16.7987;17.0499;17.2793;17.4885];  \n",
    "% Vetor de correntes medidas [A]\n",
    "Imed = [1.03150;1.03000;1.02600;1.02200;1.01800;1.01550;1.01400;1.01000;1.00350;0.988000;0.963000;0.925500;0.872500;0.807500;0.726500;0.634500;0.534500;0.427500;0.318500;0.208500;0.101000;-0.00800000;-0.111000;-0.209000;-0.303000];\n",
    "Tc = 45; %[C]\n",
    "T = Tc + 273.15; %[K]\n",
    "Ns = 36;\n",
    "\n",
    "%% Criterios de ajuste\n",
    "% var = [Iph, I0, n, Rs, Rp];\n",
    "limite_inf = [0, 0, 1, 0, 0];            % limite inferior\n",
    "limite_sup = [max(Imed)*1.1, 50*10^-6, 2, 2, 2000];  % limite superior\n",
    "\n",
    "RUNS = 30;\n",
    "pop = 20;\n",
    "maxFes = 10000; % 50000 para bons resultados\n",
    "graphic = false;\n",
    "[Iph, I0, n, Rs, Rp, RMSE] =  Jaya_PV_Original(Vmed, Imed, Ns, Tc, limite_inf, limite_sup, RUNS, pop, maxFes);\n",
    "fprintf(' Parametros obtidos:')\n",
    "fprintf('\\n Iph(A) = %f',Iph);\n",
    "fprintf('\\n I0(uA) = %f',I0*10^6);\n",
    "fprintf('\\n n = %f',n);\n",
    "fprintf('\\n Rs(ohms) = %f',Rs);\n",
    "fprintf('\\n Rp(ohms) = %f',Rp);\n",
    "fprintf('\\n RMSE = %f \\n',RMSE);\n",
    "\n",
    "\n",
    "plot(Vmed, Imed,'ro');\n",
    "hold on\n",
    "\n",
    "% plotagem do modelo obtido\n",
    "k = 1.3806503e-23; q = 1.60217646e-19;   %\n",
    "Vt = Ns*k*T/q;\n",
    "\n",
    "modelo = @(V,I) (I - Iph + I0*(exp((V+I*Rs)/(n*Vt))-1) + (V+I*Rs)/Rp);\n",
    "ezplot(modelo,[0 max(Vmed)*1.03 0 max(Imed)*1.03]);\n",
    "title(['Curva IV - Medido e Modelo'])\n",
    "xlim([0, max(Vmed)*1.03])\n",
    "ylim([0, max(Imed)*1.03])\n",
    "xlabel('Tensao (V)')\n",
    "ylabel('Corrente (A)')\n",
    "leng1 = legend('Dados', 'Modelo');\n",
    "leng1.Location = 'best';\n",
    "rmsetext = ['RMSE = ', num2str(RMSE,'%e')]; \n",
    "text(0.5, 0.25, rmsetext);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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