Transient Stability Assessment Model With Sample Selection Method Based on Spatial Distribution

IF 1.7 4区 工程技术 Q2 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS
Complexity Pub Date : 2024-10-17 DOI:10.1155/2024/5231569
Yongbin Li, Yiting Wang, Jian Li, Huanbei Zhao, Huaiyuan Wang, Litao Hu
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引用次数: 0

Abstract

With the phasor measurement units (PMUs) being widely utilized in power systems, a large amount of data can be stored. If transient stability assessment (TSA) method based on the deep learning model is trained by this dataset, it requires high computation cost. Furthermore, the fact that unstable cases rarely occur would lead to an imbalanced dataset. Thus, power system transient stability status prediction has the bias problem caused by the imbalance of sample size and class importance. Faced with such a problem, a TSA model based on the sample selection method is proposed in this paper. Sample selection aims to optimize the training set to speed up the training process while improving the preference of the TSA model. The typical samples which can accurately express the spatial distribution of the raw dataset are selected by the proposed method. Primarily, based on the location of training samples in the feature space, the border samples are selected by trained support vector machine (SVM), and the edge samples are selected by the assistance of the approximated tangent hyperplane of a class surface. Then, the selected samples are input to stacked sparse autoencoder (SSAE) as the final classifier. Simulation results in the IEEE 39-bus system and the realistic regional power system of Eastern China show the high performance of the proposed method.

Abstract Image

基于空间分布的样本选择方法的瞬态稳定性评估模型
随着相位测量单元(PMU)在电力系统中的广泛应用,可以存储大量数据。如果利用这些数据集训练基于深度学习模型的暂态稳定性评估(TSA)方法,则需要很高的计算成本。此外,不稳定情况很少发生,这也会导致数据集失衡。因此,电力系统暂态稳定状态预测存在样本量和类重要性不平衡导致的偏差问题。面对这一问题,本文提出了一种基于样本选择方法的 TSA 模型。样本选择的目的是优化训练集,以加快训练过程,同时提高 TSA 模型的偏好性。本文提出的方法选择了能准确表达原始数据集空间分布的典型样本。主要是根据训练样本在特征空间中的位置,通过训练有素的支持向量机(SVM)来选择边界样本,并借助类面的近似切线超平面来选择边缘样本。然后,将所选样本输入堆叠稀疏自动编码器(SSAE),作为最终分类器。在 IEEE 39 总线系统和现实的华东区域电力系统中的仿真结果表明,所提出的方法具有很高的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Complexity
Complexity 综合性期刊-数学跨学科应用
CiteScore
5.80
自引率
4.30%
发文量
595
审稿时长
>12 weeks
期刊介绍: Complexity is a cross-disciplinary journal focusing on the rapidly expanding science of complex adaptive systems. The purpose of the journal is to advance the science of complexity. Articles may deal with such methodological themes as chaos, genetic algorithms, cellular automata, neural networks, and evolutionary game theory. Papers treating applications in any area of natural science or human endeavor are welcome, and especially encouraged are papers integrating conceptual themes and applications that cross traditional disciplinary boundaries. Complexity is not meant to serve as a forum for speculation and vague analogies between words like “chaos,” “self-organization,” and “emergence” that are often used in completely different ways in science and in daily life.
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