A significant issue for fault classification in power distribution systems is limited fault data for training classifiers to identify power failure types for remediation. Measurement data from power systems are mostly unlabeled without specified fault types, and labeled data with confirmed fault types are very limited, posing challenges to training classifiers with sufficient accuracy. Existing fault classification methods to deal with small labeled samples explore latent structures between labeled and unlabeled data. However, this line of methods has inaccurate assumptions on the relationship between unlabeled and labeled data and suffers from accuracy loss when dealing with very limited data that are labeled. This paper proposes a novel latent structure learning under a multi-task learning framework to supplement information and deal with the challenges in limited labeled data for fault classification. The proposed method not only takes advantage of the latent structure in unlabeled data that are not effectively utilized but also overcomes the limitations of latent structure learning by preventing classifiers from being overfitted to unlabeled data. The method was validated by an experimental study from distribution-level phasor devices in a hardware-in-the-loop testbed compared with state-of-the-art fault classification algorithms. The method is also demonstrated for the robustness against measurement noise.

配电系统中故障分类的一个重要问题是用于训练分类器识别电源故障类型以进行补救的有限故障数据。来自电力系统的测量数据大多是未标记的，没有指定故障类型，并且已确认故障类型的标记数据非常有限，这对训练足够准确的分类器提出了挑战。处理小标记样本的现有故障分类方法探索标记数据和未标记数据之间的潜在结构。然而，这一系列方法对未标记数据和标记数据之间的关系有不准确的假设，并且在处理非常有限的标记数据时会遭受精度损失。本文在多任务学习框架下提出了一种新的潜在结构学习，以补充信息并应对有限标记数据中的故障分类挑战。所提出的方法不仅利用了未被有效利用的未标记数据中的潜在结构，而且通过防止分类器过度拟合未标记数据来克服潜在结构学习的局限性。该方法通过硬件在环测试台中分布级相量设备的实验研究与最先进的故障分类算法进行了验证。该方法还证明了对测量噪声的鲁棒性。所提出的方法不仅利用了未被有效利用的未标记数据中的潜在结构，而且通过防止分类器过度拟合未标记数据来克服潜在结构学习的局限性。该方法通过硬件在环测试台中分布级相量设备的实验研究与最先进的故障分类算法进行了验证。该方法还证明了对测量噪声的鲁棒性。所提出的方法不仅利用了未被有效利用的未标记数据中的潜在结构，而且通过防止分类器过度拟合未标记数据来克服潜在结构学习的局限性。该方法通过硬件在环测试台中分布级相量设备的实验研究与最先进的故障分类算法进行了验证。该方法还证明了对测量噪声的鲁棒性。该方法通过硬件在环测试台中分布级相量设备的实验研究与最先进的故障分类算法进行了验证。该方法还证明了对测量噪声的鲁棒性。该方法通过硬件在环测试台中分布级相量设备的实验研究与最先进的故障分类算法进行了验证。该方法还证明了对测量噪声的鲁棒性。