标题： 基于测量数据的海森局部线性嵌入在电气配电系统中的高效故障检测与分类 显示英文标题

标题： Efficient Fault Detection and Categorization in Electrical Distribution Systems Using Hessian Locally Linear Embedding on Measurement Data

摘要： 电力线路的故障可能会严重损害电力系统的可靠性和安全性，导致电力供应不稳定和停电风险增加。 它们构成了重大的安全威胁，需要迅速检测和缓解，以保持电力基础设施的完整性并确保持续供电。 因此，准确检测和分类电气故障对于优化电力系统维护和运行至关重要。 在本研究中，我们提出了一种新方法，利用Hessian局部线性嵌入（HLLE）技术以及后续的t-SNE（t分布随机邻域嵌入）和高斯混合模型（GMM）进行故障检测和分类。 首先，我们使用HLLE将高维（HD）电气数据转换为低维（LD）嵌入坐标。 该技术能够有效捕捉数据中的固有变化和模式，从而实现稳健的特征提取。 接下来，我们基于嵌入坐标的特征空间进行曼-惠特尼U检验以进行故障检测。 这种统计方法使我们能够检测电气故障，提供一种高效的系统监控和控制手段。 此外，为了提高故障分类，我们采用t-SNE与GMM对检测到的故障进行聚类，将其分为不同类别。 为了评估所提出方法的性能，我们在集成太阳能农场的电力系统上进行了大量仿真。 我们的结果表明，所提出的方法在不同类型的故障以及同一故障的不同变化情况下，表现出有效的故障检测和聚类能力。 总体而言，这项研究提出了一个有效的故障检测和分类方法，有助于提升故障管理实践并防止系统故障的发生。 显示英文摘要

摘要： Faults on electrical power lines could severely compromise both the reliability and safety of power systems, leading to unstable power delivery and increased outage risks. They pose significant safety hazards, necessitating swift detection and mitigation to maintain electrical infrastructure integrity and ensure continuous power supply. Hence, accurate detection and categorization of electrical faults are pivotal for optimized power system maintenance and operation. In this work, we propose a novel approach for detecting and categorizing electrical faults using the Hessian locally linear embedding (HLLE) technique and subsequent clustering with t-SNE (t-distributed stochastic neighbor embedding) and Gaussian mixture model (GMM). First, we employ HLLE to transform high-dimensional (HD) electrical data into low-dimensional (LD) embedding coordinates. This technique effectively captures the inherent variations and patterns in the data, enabling robust feature extraction. Next, we perform the Mann-Whitney U test based on the feature space of the embedding coordinates for fault detection. This statistical approach allows us to detect electrical faults providing an efficient means of system monitoring and control. Furthermore, to enhance fault categorization, we employ t-SNE with GMM to cluster the detected faults into various categories. To evaluate the performance of the proposed method, we conduct extensive simulations on an electrical system integrated with solar farm. Our results demonstrate that the proposed approach exhibits effective fault detection and clustering across a range of fault types with different variations of the same fault. Overall, this research presents an effective methodology for robust fault detection and categorization in electrical systems, contributing to the advancement of fault management practices and the prevention of system failures.