i have written the following code for pso based mppt but the simulink model gets stuck at one value and the duty does not change.... kindly suggest me the error

Question

puneet joshi il 5 Apr 2016

0
Link

Link diretto a questa domanda

https://it.mathworks.com/matlabcentral/answers/277323-i-have-written-the-following-code-for-pso-based-mppt-but-the-simulink-model-gets-stuck-at-one-value

Risposto: Nisar Ahmed il 30 Apr 2020

function D = PSO(Param, V, I)
% MPPT controller based on the Perturb & Observe algorithm.
% D output = Duty cycle of the boost converter (value between 0 and 1)
%
% V input = PV array terminal voltage (V)
% I input = PV array current (A)
%
% Param input:
%Dinit = Param(1);  %Initial value for D output
Dmax = Param(2);   %Maximum value for D
Dmin = Param(3);   %Minimum value for D
deltaD = Param(4); %Increment value used to increase/decrease the duty cycle D
persistent D_mat_cur Power_old vel max_power_new max_power_old Power_new global_best_duty self_best_duty D_mat_new
pop_size=10;
vmin=-1*deltaD;
vmax=deltaD;
%%wmax=0.9;
%%wmin=0.4;
c1=2;
C=1;
c2=2;
if isempty(Power_old)
      %%intiallize all matrices
      Power_old=zeros(1,pop_size);
      Power_new=zeros(1,pop_size);
      vel=zeros(1,pop_size);
      D_mat_new=zeros(1,pop_size);
      D_mat_cur=rand(1,pop_size);
      vel=rand(1,pop_size).*(vmax-vmin)+vmin;
      for i=1 : pop_size
          if (D_mat_cur(i)>Dmax)
              D_mat_cur(i)=Dmax;
          end
      end
      for i=1 : pop_size
          if (D_mat_cur(i)<Dmin)
              D_mat_cur(i)=Dmin;
          end
      end
      for iter =1:pop_size
          D=D_mat_cur(iter);
          pause(0.0001)
          Power_old(iter)=V*I;
      end
      [max_power_old,global_index]=max(Power_old);
      self_best_duty=D_mat_cur;
      global_best_duty=D_mat_cur(1,global_index);
  end
iter=0;
max_iter=100;
while iter<max_iter
      iter=iter+1;
      %calculate power
      for iter =1:pop_size
          D=D_mat_cur(iter);
          pause(0.0001)
          Power_new(iter)=V*I;
      end
      %%update self best duty
      for i=1:pop_size
          if (Power_new(1,i)>Power_old(1,i))
              Power_old(1,i)=Power_new(1,i);
              self_best_duty(1,i)=D_mat_cur(1,i);
          end
      end
      %%update global best duty
      [max_power_new,index]=max(Power_old);
      if (max_power_new>max_power_old)
          max_power_old=max_power_new;
          global_best_duty=D_mat_cur(1,index);
      end
   w=.001;   
      %%calculation of new velocity matrix
      for i=1 :pop_size
          vel(1,i)= C*(w.*vel(1,i) +c1*rand*(self_best_duty(1,i) - D_mat_cur(1,i))  +c2*rand*(global_best_duty-D_mat_cur(1,i)));
      end
      %%update positions
      D_mat_new=D_mat_cur+ vel;
      %%bring back the param that go out of the bounds
      %%upper limit
      for i=1 : pop_size
          if (D_mat_new(1,i)>Dmax)
              D_mat_new(1,i)=Dmax;
          end
      end
      %%lower limit
      for i=1 : pop_size
          if (D_mat_new(1,i)<Dmin)
              D_mat_new(1,i)=Dmin;
          end
      end
D_mat_cur=D_mat_new; 
end
[~,global_ind]=max(Power_new);
 D=D_mat_cur(1,global_ind);

14 Commenti
Mostra 12 commenti meno recentiNascondi 12 commenti meno recenti

kamran etivand il 23 Nov 2017

Modificato: Walter Roberson il 23 Nov 2017

Apri in MATLAB Online

function Dnew = PSO(Vpv,Ipv)
%%Problem Definition
persistent particle_Position particle_Cost particle_Velocity GlobalBest_Position ...
    particle_Best_Position particle_Best_Cost GlobalBest_Cost
% persistent
nVar=1;             % Number of Decision Variables
VarSize=[1 nVar];   % Size of Decision Variables Matrix
VarMin=0.02;         % Lower Bound of Variables
VarMax=0.95;         % Upper Bound of Variables
%%PSO Parameters
MaxIt=2;      % Maximum Number of Iterations
nPop=3;        % Population Size (Swarm Size)
phi1=2.05;
phi2=2.05;
phi=phi1+phi2;
chi=2/(phi-2+sqrt(phi^2-4*phi));
w=chi;          % Inertia Weight
wdamp=1;        % Inertia Weight Damping Ratio
c1=chi*phi1;    % Personal Learning Coefficient
c2=chi*phi2;    % Global Learning Coefficient
% Velocity Limits
VelMax=0.1*(VarMax-VarMin);
VelMin=-VelMax;
%%Initialization
if isempty(particle_Position)
   particle_Position=zeros(nPop,1);
end
if isempty(particle_Cost)
   particle_Cost=zeros(nPop,1);
end
if isempty(particle_Velocity)
  particle_Velocity=zeros(nPop,1);
end
if isempty(particle_Best_Position)
  particle_Best_Position=zeros(nPop,1);
end
if isempty(particle_Best_Cost)
  particle_Best_Cost=zeros(nPop,1);
end
if isempty(GlobalBest_Cost)
  GlobalBest_Cost=0;
end
if isempty(GlobalBest_Position)
  GlobalBest_Position=unifrnd(VarMin,VarMax,VarSize);
end
BestCost=zeros(MaxIt,1);
nfe=zeros(MaxIt,1);
% PSO Main Loop
for it=1:MaxIt
      for i=1:nPop
          Dnew= particle_Position(i);
      particle_Cost(i)=Vpv*Ipv;
      if particle_Cost(i)>particle_Best_Cost(i)
              particle_Best_Position(i)=particle_Position(i);
              particle_Best_Cost(i)=particle_Cost(i);
            end  
              % Update Global Best
              if particle_Best_Cost(i)>GlobalBest_Cost
                  GlobalBest_Position=particle_Best_Position(i)
                  GlobalBest_Cost=particle_Best_Cost(i);
              end
      for i=1:nPop
          % Update Velocity
          particle_Velocity(i) = w*particle_Velocity(i) ...
              +c1*rand(VarSize)*(particle_Best_Position(i)-particle_Position(i)) ...
              +c2*rand(VarSize)*( GlobalBest_Position-particle_Position(i));
          % Apply Velocity Limits
          particle_Velocity(i) = max(particle_Velocity(i),VelMin);
          particle_Velocity(i) = min(particle_Velocity(i),VelMax);
          % Update Position
          particle_Position(i) = particle_Position(i) + particle_Velocity(i);
          % Apply Position Limits
          particle_Position(i) = max(particle_Position(i),VarMin);
          particle_Position(i) = min(particle_Position(i),VarMax);
          % Evaluation
      end
      end
       w=w*wdamp;
  end
     Dnew=GlobalBest_Position
  end