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Latin hypercube design (LHD) is one of the most frequently used sampling methods. However, most LHDs generate data samples in a manner that hinders computational efficiency and space-filling performance when high dimensions and large samples are involved. Therefore, a sequential recursive evolution Latin hypercube design (RELHD) is proposed in this article, which adopts a permutation inheritance algorithm to update and optimize the LHD. A recursive split algorithm is also proposed and used to enhance the computational efficiency by dividing the sample set into smaller subsets. Numerical experiments demonstrate that the space-filling quality of the RELHD compares well with the enhanced stochastic evolutionary algorithm (ESE) in complex problems with large samples and high dimensions, with RELHD having a significantly higher computational efficiency than ESE. Finally, the sequential approach of RELHD proves to be a more efficient strategy when dealing with sampling-based analysis problems. [ABSTRACT FROM AUTHOR]

The electronic power transformer has many advantages, such as reactive power compensation, voltage regulation, harmonic suppression, and power factor correction. When a three-phase electronic power transformer with a star configuration is applied in the distribution network, three-phase load unbalance will cause three-phase DC voltage unbalance if the electronic power transformer controller generates improper asymmetric voltage signals, which may trigger over-voltage protection. The effects of three-phase load unbalance to the work condition of the electronic power transformer are analyzed, and a control algorithm is proposed to balance three-phase DC voltages in this article. Meanwhile, input stage control, isolation stage control, output stage control, and individual DC voltage balancing control are presented. Simulation and lab experimental results show that the electronic power transformer still works properly even under serious unbalanced load conditions. [ABSTRACT FROM AUTHOR]