High impedance Arcing faults (HIAFs) are normally caused by ruptured conductors, leaning of a tree with high impedance, and/or the presence of a high impedance object between the conductor and earth. In such cases, protections available in the microgrid may not be capable of detecting the HIAFs. Hence, to increase the safety level and reliability of the microgrid, it is essential to take action for fast and reliable detection of these types of faults. Therefore, the present study introduces an appropriate strategy to detect HIAFs using a pattern recognition approach. To this end, different scenarios are implemented in the training data extraction step considering the measurement units embedded in a 25 kV microgrid in the presence of Distributed Generations (DG) and Renewable Energy Sources (RESs) in the EMTP-RV software environment. Then, after the initial processing, the scenarios are scaled-down and compared using the Pearson Correlation Coefficient (PCC) and Principal Component Analysis (PCA) methods. Next, the processed data is classified using the Support Vector Machine (SVM) method by selecting the most appropriate kernel. Simulation results in EMTP-RV and MATLAB environments demonstrate that the proposed strategy is capable of fast detection of HIAFs in microgrids with a high level of accuracy.

高阻抗电弧故障（HIAF）通常是由导体破裂，高阻抗树的倾斜和/或导体与地面之间存在高阻抗物体引起的。在这种情况下，微电网中可用的保护措施可能无法检测HIAF。因此，为了提高微电网的安全性和可靠性，必须采取措施快速，可靠地检测这些类型的故障。因此，本研究介绍了一种使用模式识别方法检测HIAF的适当策略。为此，在EMTP-RV软件环境中，在存在分布式发电（DG）和可再生能源（RES）的情况下，考虑将测量单元嵌入25 kV微电网中，在训练数据提取步骤中实现了不同的方案。然后，经过初始处理后，将场景按比例缩小并使用Pearson相关系数（PCC）和主成分分析（PCA）方法进行比较。接下来，通过选择最合适的内核，使用支持向量机（SVM）方法对处理后的数据进行分类。在EMTP-RV和MATLAB环境中的仿真结果表明，所提出的策略能够以较高的准确度快速检测微电网中的HIAF。