目录
[2.1 simulink仿真程序](#2.1 simulink仿真程序)
[2.2 GA模块源程序](#2.2 GA模块源程序)
[2.3 PSO模块源程序](#2.3 PSO模块源程序)
[3.1 基于GA优化的MPPT](#3.1 基于GA优化的MPPT)
[3.2 基于PSO优化的MPPT](#3.2 基于PSO优化的MPPT)
一、背景
MPPT策略能够显著提高光伏、风电等发电效率，节省大量成本。该策略的经典算法是：采用遗传算法（GA）和粒子群优化算法（PSO）进行MPPT控制器中调节器占空比的实时寻优。
只有将这两种经典算法吃明白了，才算入了门，可以在后续MPPT策略改进中游刃有余，答辩时也能信手拈来。
GA和PSO策略不多赘述，核心在于此时在线优化，而非离线优化。即以实时局部最优策略进行MPPT控制。建议大家吃透源程序
二、源程序及结果
在线优化有源程序基于遗传算法（GA）和粒子群优化（PSO）算法的MPPT控制策略资源-CSDN文库
2.1 simulink仿真程序
2.2 GA模块源程序
Matlab
function D = GSA(Vpv,Ipv)
coder.extrinsic('randi')
persistent u;
persistent dcurrent;%store current duty cycle
persistent pbest;%store local best dc for power
persistent force; %store force
persistent acceleration; %store acceleration
persistent mass; % mass
persistent q; % strength of mass
persistent p; % power for each particle
persistent p_current; % power current for each particle
persistent p_min; % power min for each particle
persistent worse; %store best worse of each particle
persistent dc; %store duty cycle ~ position
persistent v; %velocity
persistent counter; %delay iteration
persistent iteration;
persistent gbest;%store global best dc for power
%initialization
max_iter = 3000;
if(isempty(counter))
counter = 0;
dcurrent = 0.5;
gbest = 0.5;
pbest = zeros(3,1);
worse = zeros(3,1);
v = zeros (3,1);
force = zeros(3,1);
mass = zeros(3,1);
q = zeros(3,1);
p = zeros(3,1);
p_current = zeros(3,1);
p_min=zeros(3,1);
acceleration=zeros(3,1);
u = 0;
dc = zeros (3,1);
iteration = 1;
%initialize position for each particle
dc(1)=0.69;
dc(2)= 0.7;
dc(3)=0.8;
end
if(counter >=1 && counter < 3000)
D=dcurrent;
counter= counter+1;
return;
end
if(u>=1 && u<=3)
p_current(u) = Vpv*Ipv;
if((Vpv*Ipv)>=p(u))
p(u) = Vpv*Ipv;
pbest(u)=dcurrent;
end
if(Vpv*Ipv < p_min(u))
p_min(u) = Vpv*Ipv;
worse(u) = dcurrent;
end
end
u=u+1;
if(u==5)
u=1;
end
if(u >= 1 && u <= 3)
%Avoid over shooting
if(iteration < max_iter)
D=dc(u);
dcurrent=D;
counter=1;
return;
else
D = dcurrent;
return
end
elseif(u==4)
iteration = iteration +1;
[~,i]=max(p);
gbest=pbest(i);
D=gbest;
dcurrent=D;
counter=1;
%Calculate strength of mass
for i = 1:3
q(i) = (p_current(i) - worse(i))/(pbest(i)-worse(i));
end
%Calculate sum of strength of mass
sum_strength_of_mass = q(1) + q(2) + q(3);
%Calculate mass
for i = 1:3
mass(i) = q(i)/sum_strength_of_mass;
end
%Calculate force
alpha = 200;
G0 = 1;
G = G0 * exp(-alpha*iteration/max_iter);
%G = 6.67430 * 10^-13; %gravitational constant
e = 2.2204*10^-16;
force(1) = rand()*G*(mass(3)*mass(1)*(dc(3)-dc(1))/(Euclidian_distance(dc(3),dc(1))+e) + mass(2)*mass(1)*(dc(3)-dc(1))/(Euclidian_distance(dc(3),dc(1))+e));
force(2) = rand()*G*(mass(3)*mass(2)*(dc(3)-dc(2))/(Euclidian_distance(dc(3),dc(2))+e) + mass(1)*mass(2)*(dc(1)-dc(2))/(Euclidian_distance(dc(1),dc(2))+e));
force(3) = rand()*G*(mass(2)*mass(3)*(dc(2)-dc(3))/(Euclidian_distance(dc(2),dc(3))+e) + mass(1)*mass(3)*(dc(1)-dc(3))/(Euclidian_distance(dc(1),dc(3))+e));
%Avoid over shooting
if(iteration == max_iter)
D=dcurrent;
return;
end
%Calculate acceleration
for i = 1:3
acceleration(i) = force(i)/mass(i);
end
for i=1:3
v(i)=updatevelocity(v(i),acceleration(i));
dc(i)=updateduty(dc(i),v(i));
end
return;
else
D=0.5;
end
end
function d = Euclidian_distance(d1,d2)
d = sqrt(d1^2+d2^2);
end
function vfinal=updatevelocity(velocity,acceleration)
vfinal = rand()*velocity + acceleration;
end
function dfinal=updateduty(d,velocity)
dup=d+velocity;
if(dup>1)
dfinal=1;
elseif(dup<0.1)
dfinal=0.1;
else
dfinal=dup;
end
end2.3 PSO模块源程序
Matlab
function D = PSO(Vpv, Ipv)
coder.extrinsic('randi')
persistent localbest globalbest k p dc Pbest Pprev dcurrent u v temp;
c1 = 1;
c2 = 2;
P = Ipv * Vpv;
if isempty(globalbest)
k = 0;
dc = zeros(3,1);
dc(1)=randi( [5 330])/1000;
dc(2)=randi( [330 660])/1000;
dc(3)=randi( [660 995])/1000;
p = zeros(3,1);
localbest = zeros(3,1);
v = zeros(3,1);
Pbest = Ipv * Vpv;
Pprev = 0;
dcurrent = 0.5;
globalbest = dcurrent;
u=0;
temp = 0;
end
D=dcurrent;
if (temp < 0)
temp = temp + 1;
return;
end
if (P > Pbest)
Pbest = P;
end
if (k < 3000)
k=k+1;
return;
else
k=0;
end
if abs(P - Pprev) < 1
if abs(P - Pbest) > 15
dc(1)=randi( [5 330])/1000;
dc(2)=randi( [330 660])/1000;
dc(3)=randi( [660 995])/1000;
v = zeros(3,1);
localbest = zeros(3,1);
p = zeros(3,1);
u= 0;
end
end
if(u>=1 && u<=3)
if(P>p(u))
p(u)=P;
localbest(u)=dcurrent;
end
end
u=u+1;
if (u > 4)
u=1;
end
if (u == 4)
[~,idx]=max(p);
globalbest=localbest(idx);
D = globalbest;
dcurrent=D;
for j=1:3
v(j)=updatevelocity(c1,c2,v(j),localbest(j),dc(j),globalbest);
dc(j)=updateduty(dc(j),v(j));
end
else
D=dc(u);
dcurrent=dc(u);
Pprev = P;
end
end
function vfinal=updatevelocity(c1,c2,velocity,pobest,d,gwbest)
% PSO Parameters
vfinal = (0.1*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=abs(dup);
else
dfinal=dup;
end
end三、程序运行结果
3.1 基于GA优化的MPPT
3.2 基于PSO优化的MPPT
四、源程序获取
上述章节已经给出了框图和源码，但一定要喂到嘴里，下载即可：