Probabilistic Calculation of Tidal Currents forWind Powered Systems Using PSO Improved LHS  

作　　者：Hongsheng Su Shilin Song Xingsheng Wang 

机构地区：[1]School of Automation and Electrical Engineering,Lanzhou Jiaotong University,Lanzhou,730070,China

出　　处：《Energy Engineering》2024年第11期3289-3303,共15页能源工程（英文）

摘　　要：This paper introduces the Particle SwarmOptimization(PSO)algorithmto enhance the LatinHypercube Sampling(LHS)process.The key objective is to mitigate the issues of lengthy computation times and low computational accuracy typically encountered when applying Monte Carlo Simulation(MCS)to LHS for probabilistic trend calculations.The PSOmethod optimizes sample distribution,enhances global search capabilities,and significantly boosts computational efficiency.To validate its effectiveness,the proposed method was applied to IEEE34 and IEEE-118 node systems containing wind power.The performance was then compared with Latin Hypercubic Important Sampling(LHIS),which integrates significant sampling with theMonte Carlomethod.The comparison results indicate that the PSO-enhanced method significantly improves the uniformity and representativeness of the sampling.This enhancement leads to a reduction in data errors and an improvement in both computational accuracy and convergence speed.

关 键 词：Latin hypercube sampling Monte Carlo simulation probabilistic currents particle swarm algorithm significant sampling 

分 类 号：TM614[电气工程—电力系统及自动化]