Probabilistic Load Flow Calculation Method Based on Polynomial Normal Transformation and Extended Latin hypercube

With a large number of distributed photo-voltaic access distribution networks, the probabilistic power flow calculation method that combines the strong correlation of multi-dimensional photovoltaic output and the improvement of computational efficiency is of great significance for the power quality assessment of high-ratio renewable energy power systems. In view of the low fitting accuracy of the assumed PV output obeying the Beta distribution in photovoltaic power generation, this paper uses the cubic spline function to reconstruct the PV output, uses the polynomial normal trans-formation technique to process the correlation of the PV output, and uses the slice sampling algorithm to generate the initial sample. Applying the extended Latin hypercube sampling to slice sampling techniques, the proposed method in this paper effectively reduces the error caused by traditional Latin hypercube sampling and improves the computational efficiency. The IEEE-34 standard node system is used as an example to simulate in Matlab. The results verify the validity and accuracy of the proposed method.