33-Node Distribution Network Power Flow Calculation

The 33-node distribution network power flow calculation is a crucial component in power system analysis, primarily used to evaluate voltage distribution, line power losses, and nodal power balance during steady-state grid operation. The core objective of power flow calculation is to determine the voltage magnitude and phase angle at each node, subsequently computing the power distribution across the entire network.

The 33-node model is commonly employed to simulate small and medium-sized distribution networks, incorporating data such as nodal admittance matrices, load powers, and generator outputs. Power flow calculations are typically implemented using Newton-Raphson or Fast Decoupled methods, which efficiently handle iterative solutions for nonlinear equation systems through matrix operations and convergence algorithms.

Program implementation generally involves these key computational steps: Data Input: The program includes built-in 33-node data containing active/reactive power loads, line impedance parameters, and network configuration details stored in structured arrays or matrices. Admittance Matrix Formation: Constructs the nodal admittance matrix based on network topology and line parameters using sparse matrix techniques to represent electrical connectivity between nodes. Iterative Power Flow Solution: Implements Newton-Raphson or PQ decomposition algorithms with while-loop iterations, Jacobian matrix updates, and mismatch calculations until voltage/power errors converge within tolerance thresholds. Result Output: After computation, outputs nodal voltage magnitudes, phase angles, and branch power flows through formatted tables or visualization modules, facilitating subsequent grid optimization or fault analysis.

For distribution networks, the 33-node model serves as a representative benchmark for validating algorithm accuracy and computational efficiency. In practical applications, this program can be extended to support more complex scenarios through modular coding approaches, such as distributed generation integration or dynamic load variation analysis with time-series simulations.