Latin Hypercube Sampling Algorithm in Power Flow Calculation

In power systems, power flow calculation serves as a critical computational task that assists engineers in designing, planning, and controlling electrical grids more effectively. The Latin Hypercube Sampling algorithm represents a widely-adopted power flow computation method that enhances computational efficiency through structured random sampling. This implementation features multidimensional stratification techniques that ensure better coverage of the input probability space compared to simple random sampling. The provided source code demonstrates key algorithmic components including: 1) Domain stratification and sample point allocation across probability distributions, 2) Random permutation techniques for dimension-wise sampling coordination, and 3) Integration with power flow equations for uncertainty propagation. By utilizing this code for probabilistic power flow calculations, engineers can more effectively address system uncertainties and solve complex problems in modern power systems through Monte Carlo-style simulations with reduced computational requirements.