function [D,iteration1] = PGWO(Vpv,Ipv)
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%% JKD Power and Energy Solutions
%% Grey Wolf optimization based solar PV MPPT
%% For mutiple shadding patterns
dim = 1;
m = 5;
persistent dc
persistent fitness
persistent D_new;
persistent alpha;
persistent alpha_pos Beta_pos ;
persistent iteration Beta_score Delta_score Delta_pos;
persistent Gmax;
persistent i
persistent tol
if(isempty(iteration))
iteration=0;
tol=100;
end
if(isempty(D_new))
D_new = 0.5;
end
% if(isempty(Gbest))
% Gbest = 0.5;
% end
if(isempty(Gmax))
Gmax=0;
end
if(isempty(alpha))
alpha=0;
end
if (isempty(Beta_pos))
alpha_pos=zeros(1,dim);
Beta_pos=zeros(1,dim);
Delta_pos=zeros(1,dim);
Beta_score=0;
Delta_score=0;
end
if(isempty(alpha))
alpha=0;
end
if (isempty(alpha_pos))
alpha_pos=zeros(1,dim);
end
if(isempty(dc))
dc=zeros(5,1);
dc(1)=0.1;
dc(2)=0.2;
dc(3)=0.3;
dc(4)=0.4;
dc(5)=0.5;
% dc(6)=0.6;
% dc(7)=0.7;
% dc(8)=0.8;
% dc(9)=0.9;
% dc(10)=1;
end
if (isempty(i))
i=0;
D = 0.5;
end
% D = 0.5;
iteration1 = 0;
if (iteration <100 )
iteration1=iteration;
if i<5
i = i+1;
Flag4ub=dc(i,:)>1;
Flag4lb=dc(i,:)<0;
dc(i)=(dc(i).*(~(Flag4ub+Flag4lb)))+1.*Flag4ub;
fitness=Vpv*Ipv;
tol = abs(alpha-fitness);
if fitness>alpha
alpha=fitness; % Update alpha
alpha_pos=dc(i);
end
if fitness<alpha && fitness>Beta_score
Beta_score=fitness; % Update beta
Beta_pos=dc(i);
end
if fitness<alpha && fitness<Beta_score && fitness>Delta_score
Delta_score=fitness; % Update delta
Delta_pos=dc(i);
end
D_new =dc(i);
D = dc(i);
return;
end
iteration = iteration+1;
i = 0;
d_alpha=updateal(alpha_pos,dc);
x1=updateX1(alpha_pos,d_alpha);
d_beta=updatebe(beta_pos,dc);
x2=updateX2(Beta_pos,d_beta);
d_delta=updatede(Delta_pos,dc);
x3=updateX3(Delta_pos,d_delta);
dc=x1+x2+x3/3;
end
D = dc;
end
function D_alpha=updateal(Alpha_pos,DC)
r2=rand(); % r2 is a random number in [0,1]
C1=2*r2; % Equation (3.4)
D_alpha=abs(C1*Alpha_pos-DC);
end
function X=updateX1(Alpha_pos,D_alpha)
r1=rand();
a=2-iteration*((2)/100);
A1=2*a*r1-a;
X1=Alpha_pos-A1*D_alpha;
X=X1;
end
function D_beta=updatebe(beta_pos,DC)
r2=rand();
C2=2*r2; % Equation (3.4)
D_beta=abs(C2*beta_pos-DC); % Equation (3.5)-part 2
end
function X=updateX2(beta_pos,D_beta)
r1=rand();
a=2-iteration*((2)/100);
A2=2*a*r1-a;
X2=beta_pos-A2*D_beta;
X=X2;
end
function D_delta=updatede(delta_pos,DC)
r2=rand();
C3=2*r2; % Equation (3.4)
D_delta=abs(C3*delta_pos-DC);
end
function X=updateX3(delta_pos,D_delta)
r1=rand();
a=2-iteration*((2)/100);
A2=2*a*r1-a;
X3=delta_pos-A2*D_delta;
X=X3;
end
%update velocity and duty cycle
v(1)=updatevelocity(v(1),pbest(1),dc(1),gbest)
v(2)=updatevelocity(v(2),pbest(2),dc(2),gbest)
v(3)=updatevelocity(v(3),pbest(3),dc(3),gbest)
v(4)=updatevelocity(v(4),pbest(4),dc(4),gbest)
%update duty cycle
dc(1)=updateduty(dc(1),v(1))
dc(2)=updateduty(dc(2),v(2))
dc(3)=updateduty(dc(3),v(3))
dc(4)=updateduty(dc(4),v(4))
return;
else
u
y='hello'
D=0.1
end
end
function vfinal=updatevelocity(velocity,pobest,d,gwbest)
% PSO Parameters
w=0.4;
%w=(1.1-(pobest/gwbest)); % Inertia Weight
% Inertia Weight Damping Ratio
c1=1.2; % Personal Learning Coefficient
c2=2; % Global Learning Coefficient
% If you would like to use Constriction Coefficients for PSO,
% uncomment the following block and comment the above set of parameters.
% % Constriction Coefficients
% 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
vfinal = (w*velocity)+(c1*rand(1)*(pobest-d))+(c2*rand(1)*(gwbest-d));
end
function dfinal=updateduty(d,velocity)
dup=d+velocity;
if(dup>1)
dfinal=1;
elseif(dup<0)
dfinal=0;
else
dfinal=dup;
end
end
