Facing the difficulty of expensive and trivial data collection and annotation, how to make a deep learning-based short-term voltage stability assessment (STVSA) model work well on a small training dataset is a challenging and urgent problem. Although a big enough dataset can be directly generated by contingency simulation, this data generation process is usually cumbersome and inefficient; while data augmentation provides a low-cost and efficient way to artificially inflate the representative and diversified training datasets with label preserving transformations. In this respect, this paper proposes a novel deep-learning intelligent system incorporating data augmentation for STVSA of power systems. First, due to the unavailability of reliable quantitative criteria to judge the stability status for a specific power system, semi-supervised cluster learning is leveraged to obtain labeled samples in an original small dataset. Second, to make deep learning applicable to the small dataset, conditional least squares generative adversarial networks (LSGAN)-based data augmentation is introduced to expand the original dataset via artificially creating additional valid samples. Third, to extract temporal dependencies from the post-disturbance dynamic trajectories of a system, a bi-directional gated recurrent unit with attention mechanism based assessment model is established, which bi-directionally learns the significant time dependencies and automatically allocates attention weights. The test results demonstrate the presented approach manages to achieve better accuracy and a faster response time with original small datasets. Besides classification accuracy, this work employs statistical measures to comprehensively examine the performance of the proposal.
翻译:面对昂贵和微不足道的数据收集和批注的困难,如何使一个基于学习的短期电压稳定性评估(STVSA)模型在小型培训数据集上顺利地发挥作用,是一个具有挑战性和紧迫的问题。虽然应急模拟可以直接产生足够大的数据集,但这种数据生成过程通常十分繁琐且效率低下;虽然数据增强提供了一种低成本和高效率的方法,以人为地将具有代表性和多样化的培训数据集与标签的维护变换一起膨胀。在这方面,本文件提出一个新的深层次的分类智能系统,包括STVSA电力系统的数据增强。首先,由于缺乏可靠的量化标准来判断具体电力系统的稳定状况,利用半监督的集群学习在原始的小型数据集中获取贴标签的样本。第二,使深层次的学习适用于小型数据集,有条件的最小方格化对抗网络(LSGAN)的增强数据通过人工生成更多的有效样本来扩大原始数据集。第三,从后阶段的精确度精确度标准中提取出时间依赖性强的精确度标准,由于缺乏可靠的定量的定量标准,因此,半级集群集群集学习了这个基于经常测试机制的双向式测试的跟踪机制。