基于进化算法改进拉丁超立方抽样的概率潮流计算

Probabilistic Load Flow Based on Improved Latin Hypercube Sampling With Evolutionary Algorithm

在对电力系统安全风险评估时所需概率潮流计算的模拟法中,基于拉丁超立方抽样(Latin hypercube sampling,LHS)的蒙特卡罗(Monte Carlo,MC)模拟比简单MC模拟效率更高。但针对概率潮流问题,目前在相关性控制方面仍待改善。为提高基于LHS法的MC模拟在概率潮流计算中的效率,从两方面改进算法:一方面,对随机变量间相关系数矩阵非正定情况提出含进化算法的改进中值拉丁超立方抽样法;另一方面,为顾及概率分布的尾部特征,提出拉丁超立方重要抽样技术。对IEEE 30和IEEE 118节点系统进行考虑发电机无功出力约束的局部相关性试验,所提方法能有效地控制相关性,并具有良好的收敛性。试验结果表明该方法是有效和合理的。

In the simulation method of power system risk assessment using probabilistic load flow calculation, Monte Carlo simulation based on Latin hypercube sampling （LHS） has higher efficiency than simple Monte Carlo simulation. But there are still some problems in controlling correlation in probabilistic load flow. In order to improve the efficiency of LHS to solve the probabilistic load flow, two improvements were made. On one hand, an improved median Latin hypercube sampling method with Evolutionary Algorithm was proposed to control correlation. On the other hand, Latin hypercube important sampling technique was presented to consider the tail of distribution. The experiments about IEEE 30-bus and IEEE l l8-bus systems considering partial correlation were made. The proposed methods can effectively control correlation with good convergence. The results indicate that the proposed methods are effective and reasonable.