Abstract
Accurate fault detection and localization in electrical distribution systems is crucial, especially with the increasing integration of distributed energy resources (DERs), which inject greater variability and complexity into grid operations. In this study, FaultXformer is proposed, a Transformer encoder-based architecture developed for automatic fault analysis using real-time current data obtained from phasor measurement unit (PMU). The approach utilizes time-series current data to initially extract rich temporal information in stage 1, which is crucial for identifying the fault type and precisely determining its location across multiple nodes. In Stage 2, these extracted features are processed to differentiate among distinct fault types and identify the respective fault location within the distribution system. Thus, this dual-stage transformer encoder pipeline enables high-fidelity representation learning, considerably boosting the performance of the work. The model was validated on a dataset generated from the IEEE 13-node test feeder, simulated with 20 separate fault locations and several DER integration scenarios, utilizing current measurements from four strategically located PMUs. To demonstrate robust performance evaluation, stratified 10-fold cross-validation is performed. FaultXformer achieved average accuracies of 98.76% in fault type classification and 98.92% in fault location identification across cross-validation, consistently surpassing conventional deep learning baselines convolutional neural network (CNN), recurrent neural network (RNN). long short-term memory (LSTM) by 1.70%, 34.95%, and 2.04% in classification accuracy and by 10.82%, 40.89%, and 6.27% in location accuracy, respectively. These results demonstrate the efficacy of the proposed model with significant DER penetration.
Abstract (translated)
在电力分配系统中，准确地检测和定位故障至关重要，尤其是在分布式能源资源（DERs）的集成不断增加的情况下，这些资源向电网操作注入了更大的可变性和复杂性。在这项研究中，提出了FaultXformer，这是一种基于Transformer编码器架构的设计，旨在利用从相量测量单元(PMU)获得的实时电流数据进行自动故障分析。 该方法利用时间序列电流数据在第一阶段提取丰富的时态信息，这对于识别故障类型以及精确确定多个节点上的故障位置至关重要。在第二阶段，这些提取出的特征被处理以区分不同的故障类型，并识别分布系统中的相应故障位置。因此，这种双阶段Transformer编码器流水线能够实现高保真度的表示学习，在很大程度上提高了工作的性能。 该模型在一个由IEEE 13节点测试馈电线生成的数据集上进行了验证，模拟了20个单独的故障位置以及多种DER集成场景，并利用四个战略性部署的PMU的电流测量。为了展示强大的性能评估，采用了分层10折交叉验证方法。在交叉验证中，FaultXformer分别实现了98.76%和98.92%的平均准确率，即在故障分类中的类型识别准确性和位置识别准确性。该模型在分类精度上比传统的深度学习基准（卷积神经网络(CNN)、递归神经网络(RNN)和长短期记忆(LSTM)）高出1.70%，34.95% 和2.04%，并且在定位精度上分别高出10.82%，40.89% 和6.27%。这些结果证明了所提出模型在显著的DER渗透情况下的有效性。
URL
https://arxiv.org/abs/2602.24254
