Electrical power being the key driver for any technology driven development, an intelligent technology enabled smart grid which ensures reliable, environment-friendly and power quality certainly provides the necessary fillip to the urban intelligence. This study introduces a novel differential approach of microgrid fault detection and classification as a smart grid enabler. The proposed microgrid protection scheme (MPS) involves an initial phase of pre-processing through anti-aliasing and filtering out of noise of the retrieved system parameters. This is followed by feature extraction process using Maximal Overlap Discrete Wavelet Transform (MODWT) with an abstract wavelet family of mother wavelet ‘FejerKorovkin’ and three level of decomposition. The differential energy calculated for both three-phase current and its zero-sequence current component at each of the decomposition level of MODWT finally serves as input to an Extreme Gradient Boost (XGBoost) based machine learning model to achieve incipient fault detection and classification. The combination of MODWT and XGBoost as an intelligent MPS working upon a pre-processed de-noised system signals, hitherto untried as per the knowledge of the authors, is tested using standard IEC microgrid test model under varied topological configurations, operational modes, fault conditions, etc. The simulation results, so extensively obtained, prove the effectiveness and robustness of the proposed approach of MPS. The MPS is additionally verified on an IEEE 13 bus microgrid model to reinforce the clam of efficiency.

电力是任何技术驱动型开​​发的关键驱动力，而启用智能技术的智能电网可确保可靠，环保和电能质量，无疑为城市智能提供了必要条件。本研究介绍了一种微电网故障检测和分类的新型差分方法，将其作为智能电网使能器。拟议的微电网保护方案（MPS）涉及预处理的初始阶段，该阶段是通过抗混叠和过滤掉检索到的系统参数的噪声。接下来是使用最大重叠离散小波变换（MODWT）和母子波'FejerKorovkin'的抽象子波族以及三级分解的特征提取过程。最终，在MODWT的每个分解水平上针对三相电流及其零序电流分量计算的差分能量将作为基于极端梯度提升（XGBoost）的机器学习模型的输入，以实现早期故障检测和分类。MODWT和XGBoost的结合作为智能MPS来处理经过预处理的降噪系统信号，到目前为止，根据作者的知识，这些方法尚未使用，它们是使用标准IEC微电网测试模型在各种拓扑配置，操作模式，故障条件下进行测试的如此广泛获得的仿真结果证明了所提出的MPS方法的有效性和鲁棒性。另外，还通过IEEE 13总线微电网模型对MPS进行了验证，以增强效率。