The classification model for identifying single-phase earth ground faults in the distribution network jointly driven by physical model and machine learning

Single-phase earth ground faults are the most frequent faults likely to occur but hard to identify in a distribution system, especially in a neutral ineffectively grounded system. Targeting on this goal, a novel AdaBoost-based single-phase earth ground fault identification model is put forward. First, after depicting the zero-sequence circuit of the distribution system, a feature engineering that can reflect local and global evolutionary processes in the fault period is constructed in detail. Second, to overcome two problems, namely, different number problems between fault and non-fault samples and curse of dimension, principal component analysis is used for feature extraction, in which only a small number of low-dimension mapped features are extracted, and then transmitted into the AdaBoost-based ground fault identification model. Subsequently, this work borrows from machine learning and applies its learning curve and receiver operating characteristic curve to guide the optimization of the proposed identification model. Numerical studies verify the effectiveness and adaptability of the proposed model toward solving single-phase earth ground faults.