In this article, the Latin hypercube sampling (LHS) based probabilistic optimal power flow (P‐OPF) technique is employed to assess the performance of power systems under large wind power penetration. In the case where only wind speed samples are available, the kernel tuned polynomial expansion series are developed to recover marginal distributions of wind speeds. Because the dependence among wind speeds has a considerable impact on P‐OPF solutions, 13 Archimedean copulae are derived to represent the dependence structure of correlated wind speeds at multiple sites. The rejection sampling method and Metropolis‐Hastings algorithm are deployed to generate samples of copula models, and a midpoint LHS technique is proposed to produce correlated low discrepancy sequences for P‐OPF computation. Finally, a case study is performed on an IEEE 118‐bus system to illustrate the proposed methods.

In this paper, Johnson system and Gaussian copula are employed to model correlated random variables in power flow equations, whereby the probabilistic power flow (PPF) problem can be mapped to an independent and identically distributed probability space. The univariate dimension reduction (UDR) model is introduced to approximate the function relationship between PPF inputs and outputs. Using Beta distribution and symmetric Johnson SU distribution, two sets of orthogonal polynomials are derived to generate quadrature weights and points, and various point estimate methods (PEMs) based on the UDR model are developed to calculate the moments of PPF outputs. Finally, the proposed PEMs are verified by numerical simulations on the IEEE 57-bus system, IEEE 118-bus system, and IEEE 300-bus system.

A multitarget search algorithm for swarm robot in an unknown 3D mountain environment is proposed. Most existing 3D environment obstacle avoidance algorithms are potential field methods, which need to consider the location information of all obstacles around the robot, and they easily fall into local optima, and their calculation is complex. Furthermore, they cannot well meet the requirements of real-time obstacle avoidance characteristics of swarm robots in multiobject searches. This paper first focuses on solving the obstacle avoidance problem of swarm robots in mountain environments. A new 3D curved obstacle tracking algorithm (3D-COTA) is designed by discretizing the mountains within the detection range of robot obstacles. Then, a task assignment model and virtual force model in 2D space are extended to 3D, and a particle swarm search model with kinematic constraints is constructed, which considers the kinematic constraints and the limitations of the communication ability of the robots. Finally, a new multitarget search algorithm for swarm robot in an unknown 3D mountain environment is proposed by means of the designed 3D surface obstacle tracking algorithm. Numerical simulation results demonstrate the effectiveness of the proposed algorithm.