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In order to achieve selective ground fault protection for bus-connected Powerformers and improve the reliability of the protection scheme, this paper presents a novel stator single-line-to-ground (SLG) fault protection scheme for bus-connected Powerformers based on S-transform (ST) and bagging ensemble learning algorithm. The scheme utilizes ST to decompose the zero-sequence current signals acquired from the Powerformer terminal to obtain the amplitude-time-frequency matrix. Then, fault features extraction is presented, and three features including the transient energy, the comprehensive correlation coefficient, and the zero-sequence active power are discussed and selected as feature vectors. The calculated data set is then extracted from feature vectors and used as inputs to the bagging ensemble learning algorithm to detect faults. Simulation results have shown that, under different fault conditions, the novel scheme can detect in which Powerformer a stator SLG fault is occurring and can detect internal faults from external faults reliably even if the fault resistance is at 8000 &#x03A9;. The proposed protection scheme does not need to set the threshold value and has noise-tolerant ability. Furthermore, the proposed technique performs better than support vector machine (SVM), random forest (RF) and k-Nearest Neighbor (KNN) techniques in detecting faults.

Stator Single-Line-to-Ground Fault Protection for Bus-Connected Powerformers Based on S-Transform and Bagging Ensemble Learning