基于遗传算法的33节点配电网网络重构MATLAB实现-洪萨配资

1. 主程序文件

% 33节点配电网网络重构 - 遗传算法优化clear;clc;close all;%% 参数设置pop_size=50;% 种群大小max_gen=100;% 最大迭代次数pc=0.8;% 交叉概率pm=0.1;% 变异概率elite_rate=0.1;% 精英保留比例%% 加载33节点系统数据network_data=load_33_node_system();%% 初始化种群population=initialize_population(pop_size,network_data);%% 遗传算法主循环best_fitness=zeros(max_gen,1);avg_fitness=zeros(max_gen,1);best_individual=[];fprintf('开始33节点配电网网络重构优化...\n');forgen=1:max_gen% 计算适应度fitness=calculate_fitness(population,network_data);% 记录最优解[best_fit,best_idx]=min(fitness);best_fitness(gen)=best_fit;avg_fitness(gen)=mean(fitness);ifgen==1||best_fit<best_fitness(max(1,gen-1))best_individual=population(best_idx,:);end% 选择操作new_population=selection(population,fitness,elite_rate);% 交叉操作new_population=crossover(new_population,pc,network_data);% 变异操作new_population=mutation(new_population,pm,network_data);population=new_population;% 显示进度ifmod(gen,10)==0fprintf('代数 %d: 最优损耗 = %.4f kW, 平均损耗 = %.4f kW\n',...gen,best_fit,avg_fitness(gen));endend%% 结果显示fprintf('\n=== 优化结果 ===\n');fprintf('最优网络损耗: %.4f kW\n',best_fitness(end));fprintf('重构方案开关状态:\n');% 显示最优开关状态display_switch_status(best_individual,network_data);% 绘制收敛曲线plot_convergence(best_fitness,avg_fitness);% 验证网络连通性is_radial=check_radial_constraint(best_individual,network_data);fprintf('网络辐射状约束: %s\n',string(is_radial));%% 潮流计算验证最终结果final_loss=power_flow_analysis(best_individual,network_data);fprintf('最终潮流计算损耗: %.4f kW\n',final_loss);

2. 33节点系统数据加载

functionnetwork_data=load_33_node_system()% 33节点配电网系统参数network_data.num_nodes=33;network_data.num_branches=32;network_data.base_V=12.66;% kVnetwork_data.base_S=10;% MVA% 节点负荷数据 (kW + jkVar)network_data.loads=[0,0;% 节点1 (平衡节点)100,60;% 节点290,40;% 节点3120,80;% 节点460,30;% 节点560,20;% 节点6200,100;% 节点7200,100;% 节点860,20;% 节点960,20;% 节点1045,30;% 节点1160,35;% 节点1260,35;% 节点13120,80;% 节点1460,10;% 节点1560,20;% 节点1660,20;% 节点1790,40;% 节点1890,40;% 节点1990,40;% 节点2090,40;% 节点2190,40;% 节点2290,50;% 节点23420,200;% 节点24420,200;% 节点2560,25;% 节点2660,25;% 节点2760,20;% 节点28120,70;% 节点29200,600;% 节点30150,70;% 节点31210,100;% 节点3260,40% 节点33]/1000;% 转换为MW% 支路数据 [起始节点, 终止节点, 电阻(ohm), 电抗(ohm)]network_data.branches=[1,2,0.0922,0.0470;2,3,0.4930,0.2511;3,4,0.3660,0.1864;4,5,0.3811,0.1941;5,6,0.8190,0.7070;6,7,0.1872,0.6188;7,8,0.7114,0.2351;8,9,1.0300,0.7400;9,10,1.0440,0.7400;10,11,0.1966,0.0650;11,12,0.3744,0.1238;12,13,1.4680,1.1550;13,14,0.5416,0.7129;14,15,0.5910,0.5260;15,16,0.7463,0.5450;16,17,1.2890,1.7210;17,18,0.7320,0.5740;2,19,0.1640,0.1565;19,20,1.5042,1.3554;20,21,0.4095,0.4784;21,22,0.7089,0.9373;3,23,0.4512,0.3083;23,24,0.8980,0.7091;24,25,0.8960,0.7011;6,26,0.2030,0.1034;26,27,0.2842,0.1447;27,28,1.0590,0.9337;28,29,0.8042,0.7006;29,30,0.5075,0.2585;30,31,0.9744,0.9630;31,32,0.3105,0.3619;32,33,0.3410,0.5362];% 联络开关位置 (可操作的开关)network_data.tie_switches=[9,15,21,27,32];% 支路编号% 分段开关位置network_data.section_switches=setdiff(1:network_data.num_branches,...network_data.tie_switches);end

3. 种群初始化

functionpopulation=initialize_population(pop_size,network_data)% 初始化种群，确保辐射状结构population=zeros(pop_size,length(network_data.tie_switches));fori=1:pop_size% 随机生成可行的开关组合valid=false;while~valid% 随机选择要闭合的联络开关数量 (通常为1-3个)num_close=randi([1,3]);close_switches=randperm(length(network_data.tie_switches),num_close);% 生成染色体individual=zeros(1,length(network_data.tie_switches));individual(close_switches)=1;% 检查辐射状约束ifcheck_radial_constraint(individual,network_data)population(i,:)=individual;valid=true;endendendend

4. 适应度计算

functionfitness=calculate_fitness(population,network_data)% 计算种群中每个个体的适应度（网络损耗）pop_size=size(population,1);fitness=zeros(pop_size,1);fori=1:pop_size% 进行潮流计算得到网络损耗power_loss=power_flow_analysis(population(i,:),network_data);% 适应度为网络损耗的倒数（最小化问题）fitness(i)=power_loss;% 如果违反约束，施加惩罚if~check_radial_constraint(population(i,:),network_data)fitness(i)=fitness(i)+1000;% 惩罚项endendend

5. 潮流计算

functiontotal_loss=power_flow_analysis(individual,network_data)% 前推回代法进行潮流计算try% 构建当前开关状态下的网络拓扑[branch_status,~]=build_network_topology(individual,network_data);% 初始化电压V=ones(network_data.num_nodes,1)*network_data.base_V;delta_V=inf;tolerance=1e-6;max_iter=100;iter=0;whiledelta_V>tolerance&&iter<max_iter V_old=V;% 回代计算电流I=calculate_currents(V,network_data.loads,branch_status,...network_data.branches);% 前推计算电压V=update_voltages(I,branch_status,network_data.branches,...network_data.base_V);delta_V=max(abs(V-V_old));iter=iter+1;end% 计算总损耗total_loss=calculate_power_loss(I,branch_status,network_data.branches);catch% 如果潮流计算不收敛，返回大值作为惩罚total_loss=10000;endendfunctionI=calculate_currents(V,loads,branch_status,branches)% 计算各支路电流num_nodes=length(V);I=zeros(size(branches,1),1);% 从末端节点向前计算电流fori=size(branches,1):-1:1ifbranch_status(i)==0% 开关断开continue;endfrom_node=branches(i,1);to_node=branches(i,2);% 负荷电流S_load=loads(to_node,1)+1j*loads(to_node,2);I_load=conj(S_load/V(to_node));% 下游支路电流之和downstream_current=0;forj=i+1:size(branches,1)ifbranches(j,1)==to_node&&branch_status(j)==1downstream_current=downstream_current+I(j);endendI(i)=I_load+downstream_current;endendfunctionV=update_voltages(I,branch_status,branches,base_V)% 更新节点电压num_nodes=max(branches(:,2));V=ones(num_nodes,1)*base_V;fori=1:size(branches,1)ifbranch_status(i)==0% 开关断开continue;endfrom_node=branches(i,1);to_node=branches(i,2);R=branches(i,3);X=branches(i,4);% 电压降voltage_drop=I(i)*(R+1j*X);V(to_node)=V(from_node)-voltage_drop;endendfunctiontotal_loss=calculate_power_loss(I,branch_status,branches)% 计算总网损total_loss=0;fori=1:size(branches,1)ifbranch_status(i)==1% 只计算闭合支路R=branches(i,3);total_loss=total_loss+abs(I(i))^2*R;endendtotal_loss=real(total_loss)*1000;% 转换为kWend

6. 遗传操作函数

functionnew_population=selection(population,fitness,elite_rate)% 锦标赛选择pop_size=size(population,1);elite_size=round(pop_size*elite_rate);new_population=zeros(size(population));% 精英保留[~,elite_idx]=mink(fitness,elite_size);new_population(1:elite_size,:)=population(elite_idx,:);% 锦标赛选择剩余个体tournament_size=3;fori=elite_size+1:pop_size contestants=randperm(pop_size,tournament_size);[~,best_idx]=min(fitness(contestants));new_population(i,:)=population(contestants(best_idx),:);endendfunctionnew_population=crossover(new_population,pc,network_data)% 单点交叉pop_size=size(new_population,1);fori=1:2:pop_size-1ifrand<pc% 选择交叉点cross_point=randi([1,size(new_population,2)-1]);% 执行交叉temp1=new_population(i,:);temp2=new_population(i+1,:);new_population(i,:)=[temp1(1:cross_point),temp2(cross_point+1:end)];new_population(i+1,:)=[temp2(1:cross_point),temp1(cross_point+1:end)];% 检查约束，如果不满足则恢复if~check_radial_constraint(new_population(i,:),network_data)new_population(i,:)=temp1;endif~check_radial_constraint(new_population(i+1,:),network_data)new_population(i+1,:)=temp2;endendendendfunctionnew_population=mutation(new_population,pm,network_data)% 位变异pop_size=size(new_population,1);chrom_length=size(new_population,2);fori=1:pop_sizeifrand<pm original=new_population(i,:);valid=false;attempts=0;while~valid&&attempts<10% 随机选择变异位mut_point=randi(chrom_length);new_population(i,mut_point)=1-new_population(i,mut_point);% 检查约束ifcheck_radial_constraint(new_population(i,:),network_data)valid=true;elsenew_population(i,:)=original;attempts=attempts+1;endendendendend

7. 约束检查函数

functionis_radial=check_radial_constraint(individual,network_data)% 检查网络是否为辐射状结构[branch_status,active_branches]=build_network_topology(individual,network_data);% 构建连通性矩阵num_nodes=network_data.num_nodes;adjacency=zeros(num_nodes);fori=1:length(branch_status)ifbranch_status(i)==1from=network_data.branches(i,1);to=network_data.branches(i,2);adjacency(from,to)=1;adjacency(to,from)=1;endend% 使用BFS检查连通性和辐射状visited=false(1,num_nodes);queue=1;% 从根节点开始visited(1)=true;parent=zeros(1,num_nodes);while~isempty(queue)current=queue(1);queue(1)=[];neighbors=find(adjacency(current,:));forneighbor=neighborsif~visited(neighbor)visited(neighbor)=true;parent(neighbor)=current;queue(end+1)=neighbor;elseifneighbor~=parent(current)% 发现环is_radial=false;return;endendend% 检查是否所有节点都连通ifall(visited)is_radial=true;elseis_radial=false;endendfunction[branch_status,active_branches]=build_network_topology(individual,network_data)% 根据染色体构建网络拓扑branch_status=ones(network_data.num_branches,1);% 默认所有支路闭合% 设置联络开关状态fori=1:length(network_data.tie_switches)switch_idx=network_data.tie_switches(i);branch_status(switch_idx)=individual(i);endactive_branches=find(branch_status==1);end

8. 结果显示函数

functiondisplay_switch_status(individual,network_data)% 显示开关状态fprintf('联络开关状态:\n');fori=1:length(network_data.tie_switches)switch_idx=network_data.tie_switches(i);status=individual(i);fprintf('开关 %d (支路%d-%d): %s\n',i,...network_data.branches(switch_idx,1),...network_data.branches(switch_idx,2),...iif(status==1,'闭合','断开'));endfprintf('\n活动的支路:\n');[branch_status,active_branches]=build_network_topology(individual,network_data);fori=1:length(active_branches)branch_idx=active_branches(i);fprintf('支路 %d: 节点%d - 节点%d\n',branch_idx,...network_data.branches(branch_idx,1),...network_data.branches(branch_idx,2));endendfunctionplot_convergence(best_fitness,avg_fitness)% 绘制收敛曲线figure;plot(1:length(best_fitness),best_fitness,'b-','LineWidth',2);hold on;plot(1:length(avg_fitness),avg_fitness,'r--','LineWidth',2);xlabel('迭代次数');ylabel('网络损耗 (kW)');title('遗传算法收敛曲线');legend('最优解','平均解');grid on;endfunctionresult=iif(condition,true_val,false_val)% 内联if函数ifcondition result=true_val;elseresult=false_val;endend