用Transformer模型精准识别配电网故障类型与位置，适用于高分布式能源接入场景。
FaultXformer: A Transformer-Encoder Based Fault Classification and Location Identification model in PMU-Integrated Active Electrical Distribution System
- 基于双阶段Transformer编码器处理PMU电流时序数据，提取故障特征。
- 故障分类准确率98.76%，定位准确率98.92%，显著优于CNN、RNN、LSTM。
- 适合高比例分布式能源接入的智能配电网故障诊断，实用性强。
在分布式能源资源（DER）日益普及的背景下，电力配电系统的故障检测与定位愈发关键。本文提出FaultXformer，一种基于Transformer编码器的故障分类与定位模型，利用相量测量单元（PMU）获取的实时电流数据进行自动分析。该方法分两阶段：第一阶段从时序电流数据中提取丰富的时序特征，用于判断故障类型并精确定位多节点故障；第二阶段对特征进行处理，实现故障类型区分与位置识别。该双阶段架构实现了高保真表示学习，显著提升性能。模型在IEEE 13节点测试馈线数据集上验证，涵盖20个不同故障位置及多种DER集成场景，使用4个关键位置的PMU电流数据。采用分层10折交叉验证，平均故障分类准确率达98.76%，故障定位准确率达98.92%，分别较传统深度学习模型CNN、RNN、LSTM提升1.70%、34.95%、2.04%和10.82%、40.89%、6.27%。结果表明，该模型在高比例DER渗透下仍具高效性与鲁棒性。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。