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1. Research on Fault Reconstruction of Distribution Network Based on Genetic Algorithm [IEEE33 Node] (Matlab Code Implementation) Research on Fault Reconstruction of Distribution Network Based on Genetic Algorithm (IEEE33 Node) Matlab Code Implementation 1. Core Code Framework The following code implements fault reconstruction of IEEE33 node distribution network based on genetic algorithm, with the objective function being the weighted combination of minimizing network loss and minimizing switch action times. The code is divided into four parts: initialization, fitness calculation, genetic operation, and result visualization. %Main program: Genetic algorithm optimization for fault reconstruction of distribution network CLC; clear; close all; %Parameter setting pop_2=50; %Population size max_gen=100; %Maximum iteration times pc=0.8; %Intersection probability pm=0.1; %Mutation probability w1=0.7; w2 = 0.3; %Target function weight (network loss vs switch action)% load IEEE33 node data (topology, load, branch parameters, etc. need to be defined in advance) load ("IEEE33_data. mat"); %Including: bus, line, load, DG, and other structures, initialization population pop=init_population (pop_2, line. num_stwitch); %Main loop for gen=1: max_gen% Calculate fitness fitness=zeros (pop_2, 1); for i = 1:pop_size [loss, switch_num] = evaluate_individual(pop(i,:), bus, line, load); fitness(i) = w1 * loss + w2 * switch_num; %Objective function end% selection operation (tournament selection) new_pop=selection (pop, fitness); %Cross operation (single point cross) new_pop=crossover (new_pop, pc); %Mutation operation (bit flip) new_pop=mutation (new_pop, pm); %Update population pop=new_pop; %Record the optimal solution [best_fitness (gen), idx]=min (fitness); best_individual(gen,:) = pop(idx,:); End% visualization results plot_results (best_fitness, best_individe, bus, line); 2. Key function implementation: (1) Population initialization function pop=init_population (pop_2, num_stwitch)% Randomly generate binary encoded switch states (0=open, 1=closed) pop=randi ([0,1], pop_2, num_stwitch); %Ensure that the initial solution satisfies the radial constraint (modified by heuristic rules) for i=1: pop_stize pop (i,:)=secure_radial (pop (i,:), line. connections); Endend (2) fitness assessment function [loss, switch_num] = evaluate_individual(individual, bus, line, load) % Update branch status line. status=individual; %Perform power flow calculation (using forward backward substitution or Matpower) [V, P_loss]=power_flow (bus, line, load); %Calculate the objective function loss=sum (P_loss); %Network loss switch_num=sum (individual); %Check the voltage constraint (penalty item) for the number of switch actions% if any (abs (V)<0.95 | abs (V)>1.05) loss=1e6; %Punishment factor endend (3) Genetic operation% Selection operation (tournament selection) function new_pop=selection (pop, fitness) new_pop=zeros (size (pop)); for i = 1:size(pop,1) candidates = randperm(size(pop,1), 3); %Randomly select 3 individuals [~, idx]=min (fitness (candidates)); new_pop(i,:) = pop(candidates(idx),:); Endend% crossover operation (single point crossover) function new_pop=crossover (pop, pc) new_pop=pop; for i = 1:2:size(pop,1)-1 if rand < pcpoint = randi([1, size(pop,2)-1]); temp = new_pop(i, point+1:end); new_pop(i, point+1:end) = new_pop(i+1, point+1:end); new_pop(i+1, point+1:end) = temp; End end% mutation operation (bit flip) function new_pop=mutat

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