DC microgrids are gaining more importance in maritime, aerospace, telecom, and isolated power plants for heightened reliability, efficiency, and control. Yet, designing a protective system for DC microgrids is challenging due to novelty and limited literature. Recent interest emphasizes standalone fault detection and classification, especially through data-driven machine-learning approaches. However, the emphasis remains on progressing state-of-the-art tools for fault diagnosis in DC microgrids. Therefore, this work emphasizes fault detection and classification in a low-voltage standalone DC microgrid using a data-driven machine learning hybrid approach: bagged ensemble learner and cosine k-nearest neighbour (C-kNN) algorithms. The proposed fault detection and classification scheme makes the use of local voltage and current measurements which enhances the admissibility of the proposed scheme. The bagged ensemble learner accurately identifies the faults in the line, whereas the cosine k-nearest neighbor classifies the fault as pole to ground or pole to pole for further corrective actions. A diverse set of test scenarios encompassing faulty and normal conditions has been analyzed and validated by randomizing data inputs. The test model comprising PV, battery source, and loads have been constructed in MATLAB/Simulink environment. The proposed scheme promises accurate fault identification and classification in normal and noisy environments. To establish the robustness of the proposed approach, the outcomes of the fault detection and classification scheme have been compared with the methods reported in the literature. The results indicate that the proposed method outperformed in comparison to existing methods.

直流微电网在海事、航空航天、电信和孤立发电厂中变得越来越重要，以提高可靠性、效率和控制。然而，由于新颖性和文献有限，设计直流微电网的保护系统具有挑战性。最近的兴趣强调独立的故障检测和分类，特别是通过数据驱动的机器学习方法。然而，重点仍然是发展最先进的直流微电网故障诊断工具。因此，这项工作强调使用数据驱动的机器学习混合方法对低压独立直流微电网进行故障检测和分类：袋装集成学习器和余弦 k 最近邻 (C-kNN) 算法。所提出的故障检测和分类方案利用了本地电压和电流测量，这增强了所提出方案的可接受性。袋装集成学习器准确识别线路中的故障，而余弦 k 最近邻将故障分类为极对地或极对极，以采取进一步的纠正措施。通过随机化数据输入来分析和验证包含故障和正常条件的一组不同的测试场景。在MATLAB/Simulink环境下构建了包括PV、电池源和负载的测试模型。所提出的方案保证了在正常和噪声环境中准确的故障识别和分类。为了确定所提出方法的鲁棒性，将故障检测和分类方案的结果与文献中报道的方法进行了比较。结果表明，与现有方法相比，所提出的方法优于现有方法。