Discussion of Training Samples-Oreinted Sampling Methods for Data-driven Power Systems Analysis

Abstract

Data-driven methods such as deep neural networks (DNNs) and Gaussian processes (GPs), have been a promising way to achieve the balance of the calculation accuracy and efficiency in power system analysis. For instance, studies are utilizing the DNN-based method to achieve the fast and accurate calculation of probabilistic power flow. These methods rely on the sample data for training, which is mainly obtained by sampling. However, there is still no guidance on how to select the suitable sampling method to effectively generate the representative training samples. To address this problem, this paper explores the impact of different sampling methods on data-driven methods through theoretical analysis and simulation. Then some reasonable conclusions are drawn in this paper: i) Random sampling (RS) is suitable for data-driven methods that require large-scale training samples. ii) Latin hypercube sampling (LHS) is suitable for data-driven methods that only work well under small-scale training samples. iii) The assumption that the training samples generated by uniform sampling (US), which covers all sampling intervals, could not lead to good performance. Finally, simulations are implemented on the IEEE 39-bus system and the results demonstrate the effectiveness of the proposed conclusions