load flow by backward-forward sweep algorithm to make NR

Question

emy 2024-2-3

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编辑： emy 2024-9-8

Hello every one..

please, i need matlab code to make network reconfiguration using BPSO as optimization method for ieee 33 bus system but i need load flow by BFS algorthim for this code.

I know NR by BPSO found but using mat power to make load flow.

thanks alot.

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Omar Shaaban 2024-9-1

Hi did you happen to find the network reconfiguration code? can you please share to: omar.abdalmoneem@gmail.com

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Answer 1

Zinea 2024-9-2

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https://ww2.mathworks.cn/matlabcentral/answers/2085928-load-flow-by-backward-forward-sweep-algorithm-to-make-nr#answer_1508619

在 MATLAB Online 中打开

Hi emy,

Here is the outline of the MATLAB code for NR using BPSO with a simplified BFS-based load flow analysis for the IEEE 33-bus system. The following points are to be considered:

The voltage magnitudes are initialized to 1 p.u. (per unit) for simplicity.
The adjacency matrix represents the connectivity of the network, where a ‘1’ indicates a connection between buses (i.e., a closed branch).
The voltage drop across each branch is calculated using a simplified linear approximation. This is a basic method and should be replaced with a more precise calculation, such as using the power flow equations.

% Initialization
numBuses = 33; % Number of buses
numBranches = 37; % Number of branches
maxIter = 100; % Maximum number of iterations for BPSO
numParticles = 30; % Number of particles in the swarm
% Hypothetical bus data: [busNumber, type, Pd, Qd]
busData = [
    1, 1, 0, 0; % Slack bus
    2, 2, 0.1, 0.06;
    3, 2, 0.09, 0.04;
    % Add remaining buses...
];
% Hypothetical branch data: [fromBus, toBus, resistance, reactance]
branchData = [
    1, 2, 0.0922, 0.0470;
    2, 3, 0.4930, 0.2511;
    % Add remaining branches...
];
% Initialize particles
particles = randi([0, 1], numParticles, numBranches);
velocities = zeros(numParticles, numBranches);
pBest = particles;
gBest = particles(1, :);
pBestCost = inf(numParticles, 1);
gBestCost = inf;
% BPSO parameters
w = 0.5; % Inertia weight
c1 = 1.5; % Cognitive coefficient
c2 = 1.5; % Social coefficient
% Optimization Loop
for iter = 1:maxIter
    for i = 1:numParticles
        % Perform BFS Load Flow Analysis
        [V, loss] = bfsLoadFlow(particles(i, :), busData, branchData);
        
        % Evaluate cost function (e.g., power loss)
        cost = sum(loss);
        
        % Update personal best
        if cost < pBestCost(i)
            pBest(i, :) = particles(i, :);
            pBestCost(i) = cost;
        end
        
        % Update global best
        if cost < gBestCost
            gBest = particles(i, :);
            gBestCost = cost;
        end
    end
    
    % Update particle velocities and positions
    for i = 1:numParticles
        velocities(i, :) = w * velocities(i, :) ...
            + c1 * rand * (pBest(i, :) - particles(i, :)) ...
            + c2 * rand * (gBest - particles(i, :));
        
        % Update particles using sigmoid function for binary conversion
        sigmoid = 1 ./ (1 + exp(-velocities(i, :)));
        particles(i, :) = rand(size(sigmoid)) < sigmoid;
    end
    
    % Display iteration information
    fprintf('Iteration %d: Best Cost = %.4f\n', iter, gBestCost);
end
% Final Results
fprintf('Optimal Configuration: %s\n', num2str(gBest));
fprintf('Minimum Loss: %.4f\n', gBestCost);
function [V, loss] = bfsLoadFlow(particle, busData, branchData)
    % Initialize variables
    numBuses = size(busData, 1);
    V = ones(numBuses, 1); % Initialize voltage magnitudes to 1 p.u.
    loss = zeros(size(branchData, 1), 1); % Power loss for each branch
    % Create adjacency matrix for BFS
    adjacencyMatrix = zeros(numBuses, numBuses);
    for i = 1:size(branchData, 1)
        if particle(i) == 1 % If branch is closed
            fromBus = branchData(i, 1);
            toBus = branchData(i, 2);
            adjacencyMatrix(fromBus, toBus) = 1;
            adjacencyMatrix(toBus, fromBus) = 1;
        end
    end
    % BFS algorithm
    visited = false(numBuses, 1);
    queue = [1]; % Start from the slack bus (assumed to be bus 1)
    visited(1) = true;
    
    while ~isempty(queue)
        currentBus = queue(1);
        queue(1) = [];
        
        neighbors = find(adjacencyMatrix(currentBus, :));
        for neighbor = neighbors
            if ~visited(neighbor)
                % Find branch index
                branchIndex = find((branchData(:, 1) == currentBus & branchData(:, 2) == neighbor) | ...
                                   (branchData(:, 1) == neighbor & branchData(:, 2) == currentBus));
                
                % Calculate voltage drop and losses
                resistance = branchData(branchIndex, 3);
                reactance = branchData(branchIndex, 4);
                powerDemand = busData(neighbor, 3) + 1i * busData(neighbor, 4);
                
                % Simplified voltage drop calculation (linear approximation)
                voltageDrop = (resistance + 1i * reactance) * powerDemand / V(currentBus);
                V(neighbor) = V(currentBus) - abs(voltageDrop);
                
                % Calculate power loss in the branch
                currentFlow = powerDemand / V(currentBus);
                loss(branchIndex) = resistance * abs(currentFlow)^2;
                
                % Mark as visited and add to queue
                visited(neighbor) = true;
                queue(end + 1) = neighbor;
            end
        end
    end
end

Hope this helps you get started!

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emy 2024-9-6

编辑：emy 2024-9-8

thank you very much,

please, Zinea i want to connect with you.

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load flow by backward-forward sweep algorithm to make NR

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