Generator Rotor Electrical Asymmetry Test Rig Experiments [dataset]

This paper presents a simplified automated fault detection scheme for wind turbine induction generators with rotor electrical asymmetries. Fault indicators developed in previous works have made use of the presence of significant spectral peaks in the upper sidebands of the supply frequency harmonics; however, the specific location of these peaks may shift depending on the wind turbine speed. As wind turbines tend to operate under variable speed conditions, it may be difficult to predict where these fault-related peaks will occur. To accommodate for variable speeds and resulting shifting frequency peak locations, previous works have introduced methods to identify or track the relevant frequencies, which necessitates an additional set of processing algorithms to locate these fault-related peaks prior to any fault analysis. In this work, a simplified method is proposed to instead bypass the issue of variable speed (and shifting frequency peaks) by introducing a set of bandpass filters that encompass the ranges in which the peaks are expected to occur. These filters are designed to capture the fault-related spectral information to train a classifier for automatic fault detection, regardless of the specific location of the peaks. Initial experimental results show that this approach is robust against variable speeds, and further shows good generalisability in being able to detect faults at speeds and conditions that were not presented during training. After training and tuning the proposed fault detection system, the system was tested on ‘unseen’ data and yielded a high classification accuracy of 97.4%, demonstrating the efficacy of the proposed approach.

本论文针对存在转子电气不对称（rotor electrical asymmetries）故障的风力发电机组感应发电机，提出了一种简化的自动化故障检测方案。既往研究中开发的故障指标，多利用电源频率谐波上边带中存在的显著频谱峰值作为判断依据；然而此类峰值的具体位置会随风机转速发生偏移。由于风力发电机组通常运行于变速工况，因此难以预判这些故障相关峰值的出现位置。为适配变速工况及由此产生的频谱峰值偏移问题，既往研究引入了识别或追踪相关频率的方法，但这需要额外的处理算法集，以在开展故障分析前定位这些故障相关峰值。本研究则提出了一种简化方案，通过构建一组覆盖峰值预期出现范围的带通滤波器（bandpass filters），绕过变速工况（及频谱峰值偏移）的问题。这些滤波器用于捕获与故障相关的频谱信息，以训练用于自动故障检测的分类器（classifier），无需考虑峰值的具体位置。初始实验结果表明，该方法对变速工况具有鲁棒性，且展现出良好的泛化性（generalisability），能够在训练阶段未涉及的转速与工况下检测故障。在对所提出的故障检测系统进行训练与调优后，研究团队将其应用于“未见”数据（unseen data）集进行测试，最终获得了97.4%的高分类准确率，验证了所提方法的有效性。

搜集汇总

背景与挑战

背景概述

该数据集聚焦于风力涡轮机感应发电机转子电气不对称的故障检测研究，通过引入带通滤波器方法处理变速运行导致的频率峰值偏移问题，实现了97.4%的高分类准确率，适用于自动化故障检测和状态监测应用。数据来自杜伦大学的实验测试台，以ZIP格式提供，涵盖发电机性能测试的原始数据。