The aim of the paper is to present a newly developed approach for reliability-based design optimization. It is based on double loop framework where the outer loop of algorithm covers the optimization part of process o...The aim of the paper is to present a newly developed approach for reliability-based design optimization. It is based on double loop framework where the outer loop of algorithm covers the optimization part of process of reliability-based optimization and reliability constrains are calculated in inner loop. Innovation of suggested approach is in application of newly developed optimization strategy based on multilevel simulation using an advanced Latin Hypercube Sampling technique. This method is called Aimed multilevel sampling and it is designated for optimization of problems where only limited number of simulations is possible to perform due to enormous com- putational demands.展开更多
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 accur...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.展开更多
评估组合系统可靠性时,蒙特卡罗模拟法不受系统规模及非线性的影响,且结果准确的特性使其在大型电力系统可靠性评估中具有优越性。但为了获得精度较高的可靠性指标,其往往需要较长计算时间。针对这一问题,采用重要抽样法与离散拉丁超立...评估组合系统可靠性时,蒙特卡罗模拟法不受系统规模及非线性的影响,且结果准确的特性使其在大型电力系统可靠性评估中具有优越性。但为了获得精度较高的可靠性指标,其往往需要较长计算时间。针对这一问题,采用重要抽样法与离散拉丁超立方抽样相结合的方法,从减小样本方差与增加样本均匀性两方面提高蒙特卡罗模拟的收敛性。对于大规模发输电系统,运用灵敏度分析与线性规划相结合的方法进行系统过负荷校正,既能保证求解最优性又可以提高求解速度。将该算法应用于IEEE RTS79系统、IEEE RTS96系统和某电网500 k V及以上电压等级电力系统计算可靠性指标,验证了该算法的可行性。展开更多
基金support of projects of Ministry of Education of Czech Republic KONTAKT No.LH12062previous achievements worked out under the project of Technological Agency of Czech Republic No.TA01011019.
文摘The aim of the paper is to present a newly developed approach for reliability-based design optimization. It is based on double loop framework where the outer loop of algorithm covers the optimization part of process of reliability-based optimization and reliability constrains are calculated in inner loop. Innovation of suggested approach is in application of newly developed optimization strategy based on multilevel simulation using an advanced Latin Hypercube Sampling technique. This method is called Aimed multilevel sampling and it is designated for optimization of problems where only limited number of simulations is possible to perform due to enormous com- putational demands.
文摘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.
文摘评估组合系统可靠性时,蒙特卡罗模拟法不受系统规模及非线性的影响,且结果准确的特性使其在大型电力系统可靠性评估中具有优越性。但为了获得精度较高的可靠性指标,其往往需要较长计算时间。针对这一问题,采用重要抽样法与离散拉丁超立方抽样相结合的方法,从减小样本方差与增加样本均匀性两方面提高蒙特卡罗模拟的收敛性。对于大规模发输电系统,运用灵敏度分析与线性规划相结合的方法进行系统过负荷校正,既能保证求解最优性又可以提高求解速度。将该算法应用于IEEE RTS79系统、IEEE RTS96系统和某电网500 k V及以上电压等级电力系统计算可靠性指标,验证了该算法的可行性。
