7利用智能算法对微网中的分布式电源进行最优调度实现配电网稳定运行.zip

clear clc close all % psoSVMcgForClass % % by faruto %Email:[email protected] QQ:516667408 http://blog.sina.com.cn/faruto %last modified 2011.06.08 % 若转载请注明： % faruto and liyang , LIBSVM-farutoUltimateVersion % a toolbox with implements for support vector machines based on libsvm, 2011. % Software available at http://www.matlabsky.com % % Chih-Chung Chang and Chih-Jen Lin, LIBSVM : a library for % support vector machines, 2001. Software available at % http://www.csie.ntu.edu.tw/~cjlin/libsvm %% 参数初始化 pso_option = struct('c1',2.05,'c2',2.05,'maxgen',2500,'sizepop',100, ... 'k',0.6,'wV',1.1,'wP',1.1,'v',5, ... 'popcmax',30,'popcmin',-30,'popgmax',30,'popgmin',-30); % pso_option = struct('c1',1.5,'c2',1.7,'maxgen',200,'sizepop',20, ... % 'k',0.6,'wV',1,'wP',1,'v',5, ... % 'popcmax',100,'popcmin',0.1,'popgmax',10^3,'popgmin',10^(-2)); % c1:初始为1.5,pso参数局部搜索能力 % c2:初始为1.7,pso参数全局搜索能力 % maxgen:初始为200,最大进化数量 % sizepop:初始为20,种群最大数量 % k:初始为0.6(k belongs to [0.1,1.0]),速率和x的关系(V = kX) % wV:初始为1(wV best belongs to [0.8,1.2]),速率更新公式中速度前面的弹性系数 % wP:初始为1,种群更新公式中速度前面的弹性系数 % v:初始为5,SVM Cross Validation参数 % popcmax:初始为100,SVM 参数c的变化的最大值. % popcmin:初始为0.1,SVM 参数c的变化的最小值. % popgmax:初始为1000,SVM 参数g的变化的最大值. % popgmin:初始为0.01,SVM 参数c的变化的最小值. Vcmax = pso_option.k*pso_option.popcmax; Vcmin = -Vcmax ; Vgmax = pso_option.k*pso_option.popgmax; Vgmin = -Vgmax ; eps = 10^(-5); % train_1 = train(1:100,1); % train_label1 = train_label(1:100); % train_2 = train(31:40,1:9); % train_label2 = train_label(31:40); %% 产生初始粒子和速度 for i=1:pso_option.sizepop % 随机产生种群和速度 pop(i,1) = (pso_option.popcmax-pso_option.popcmin)*rand+pso_option.popcmin; pop(i,2) = (pso_option.popgmax-pso_option.popgmin)*rand+pso_option.popgmin; V(i,1)=Vcmax*rands(1,1); V(i,2)=Vgmax*rands(1,1); % 计算初始适应度 fitness(i)=myfunc_fit1(pop(i,:)); % [traini1,a1,b1]=svmpredict(train_label1,train_1,model); % [traini2,a2,b2]=svmpredict(train_label2,train_2,model); % fitness(i)= 0.25*mse(traini1-train_label1) + 0.75*mse(traini2-train_label2); end % 找极值和极值点 [global_fitness bestindex]=min(fitness); % 全局极值 local_fitness=fitness; % 个体极值初始化 global_x=pop(bestindex,:); % 全局极值点 local_x=pop; % 个体极值点初始化 % 每一代种群的平均适应度 avgfitness_gen = zeros(1,pso_option.maxgen); %% 迭代寻优 for i=1:pso_option.maxgen for j=1:pso_option.sizepop %速度更新 V(j,:) = pso_option.wV*V(j,:) + pso_option.c1*rand*(local_x(j,:) - pop(j,:)) + pso_option.c2*rand*(global_x - pop(j,:)); if V(j,1) > Vcmax V(j,1) = Vcmax; end if V(j,1) < Vcmin V(j,1) = Vcmin; end if V(j,2) > Vgmax V(j,2) = Vgmax; end if V(j,2) < Vgmin V(j,2) = Vgmin; end %种群更新 pop(j,:)=pop(j,:) + pso_option.wP*V(j,:); if pop(j,1) > pso_option.popcmax pop(j,1) = pso_option.popcmax; end if pop(j,1) < pso_option.popcmin pop(j,1) = pso_option.popcmin; end if pop(j,2) > pso_option.popgmax pop(j,2) = pso_option.popgmax; end if pop(j,2) < pso_option.popgmin pop(j,2) = pso_option.popgmin; end % 自适应粒子变异 if rand>0.5 k=ceil(2*rand); if k == 1 pop(j,k) = (pso_option.popcmax-pso_option.popcmin)*rand + pso_option.popcmin; end if k == 2 pop(j,k) = (pso_option.popgmax-pso_option.popgmin)*rand + pso_option.popgmin; end end %适应度值 fitness(j)=myfunc_fit1(pop(j,:)); % [trainj1,a1,b1]=svmpredict(train_label1,train_1,model); % [trainj2,a2,b2]=svmpredict(train_label2,train_2,model); % fitness(j)= 0.2*mse(trainj1-train_label1) + 0.8*mse(trainj2-train_label2); %个体最优更新 if fitness(j) < local_fitness(j) local_x(j,:) = pop(j,:); local_fitness(j) = fitness(j); end if fitness(j) == local_fitness(j) && pop(j,1) < local_x(j,1) local_x(j,:) = pop(j,:); local_fitness(j) = fitness(j); end %群体最优更新 if fitness(j) < global_fitness global_x = pop(j,:); global_fitness = fitness(j); end if abs( fitness(j)-global_fitness )<=eps && pop(j,1) < global_x(1) global_x = pop(j,:); global_fitness = fitness(j); end end fit_gen(i)=global_fitness; avgfitness_gen(i) = sum(fitness)/pso_option.sizepop; end xlswrite('fit_gen.xlsx',fit_gen); xlswrite('avgfitness_gen.xlsx',avgfitness_gen); %% 结果分析 figure; hold on; plot(fit_gen,'r-','LineWidth',1.5); % plot(avgfitness_gen,'o-','LineWidth',1.5); legend('最佳适应度','平均适应度'); xlabel('进化代数','FontSize',12); ylabel('适应度','FontSize',12); grid on; bestc = global_x(1); bestg = global_x(2); bestCVmse = fit_gen(pso_option.maxgen); line1 = '适应度曲线MSE[PSOmethod]'; line2 = ['(参数c1=',num2str(pso_option.c1), ... ',c2=',num2str(pso_option.c2),',终止代数=', ... num2str(pso_option.maxgen),',种群数量pop=', ... num2str(pso_option.sizepop),')']; line3 = ['Best c=',num2str(bestc),' g=',num2str(bestg), ... ' CVmse=',num2str(bestCVmse),]; title({line1;line2;line3},'FontSize',12);