An extended quasi monte carlo probabilistic load flow method based on non-parametric kernel density estimation

作者：Fang, Sidun^*; Cheng, Haozhong; Xu, Guodong; Yao, Liangzhong; Zeng, Pingliang

来源：Automation of Electric Power Systems, 2015, 39(7): 21-27.

DOI：10.7500/AEPS20140920005

• Kernel Density Estimation
• Latin hypercube sampling
• Latin hypercube sampling methods
• Non-normal distribution
• Non-parametric density estimators
• Probabilistic load flow
• Quasi-Monte Carlo
• Sobol sequence

The extended Monte Carlo method is capable of translating the error in the probabilistic load flow into controllable parameters and retaining the power flow results already obtained with the sample size increased. The conventional method based on the Latin hypercube sampling is more efficient than simple random sampling, but still have two main drawbacks: firstly, the Latin hypercube sampling method is of low accuracy for its incapability of generating sampling sequences of low discrepancy, which constitutes the bottleneck of convergence;secondly, there is no suitable convergence criterion that can be adopted in the non-normal distribution scenarios. In order to overcome these two drawbacks, a probabilistic load flow method based on Sobol sequence is proposed. Furthermore, a convergence criterion based on non-parametric density estimator is employed. The simulation results on IEEE 30-bus system and IEEE 118 bus system demonstrate the validity of the proposed method. In contrast to the method based on Latin hypercube sampling, the proposed method is of high efficiency, accuracy and speed. And the adoption of the convergence criterion is more direct and flexible, and better able to guide the convergence in extended probabilistic load flow.

• 出版日期2015
• 单位上海交通大学