Electric Shock Accident Detection Method Based on Ensemble Decision Trees Boosting for Feature Selection

To address the existing issue of electric shock incidents that cannot be accurately identified by current leakage protection devices, this paper presents a novel electric shock accident recognition method. Firstly, the method of singular spectrum analysis (SSA) is employed to extract the main components of leakage recording data. Subsequently, 20 temporal domain features of the leakage current waveform are extracted. Then, an ensemble learning model based on extreme gradient boosting (XGBoost), categorical boosting (CatBoost) and random forest (RF), is established to select optimal features that best represent the sample characteristics from the feature set. Finally, support vector machine (SVM) is used to classify the extracted dataset. Experimental results demonstrate that this method can rapidly differentiate between electric shock faults and common leakage faults, achieving an accuracy rate as high as 99%, indicating its feasibility.