An Ensemble Learning Method for the Fault Multi-classification of Smart Meters

With the rapid development of the power industry and the widespread adoption of smart meters, the occurrence of smart meter failures has also become more frequent. Consequently, the classification of smart meter faults has become a crucial task to ensure quality assurance in the power industry. Accurately determining the fault types of smart meters and improving maintenance efficiency are of utmost importance to ensure their safe and stable operation. Traditional methods for classifying smart meter faults primarily rely on manual inspection and testing, which suffer from issues such as low classification efficiency, high cost, susceptibility to missed detections, and false detections. In recent years, machine learning methods have demonstrated advantages in this field. This paper proposes an ensemble learning method for the multi-classification of smart meter faults to enhance the efficiency and accuracy of fault classification. Firstly, various data preprocessing techniques are employed to clean and extract features from a real-world dataset, thereby enhancing the data quality of the smart meter fault types. Secondly, a selection process is conducted to screen classical machine learning algorithms, resulting in the choice of three algorithms: K Nearest Neighbors (KNN), Random Forest (RF), and Xtreme Gradient Boosting (XGBoost). These algorithms are then utilized to classify the fault types of smart meters. Finally, a multi-classification ensemble learning method is introduced to combine the results from multiple classifiers, thereby improving the accuracy and robustness of smart meter fault classification. Experimental results demonstrate that the proposed method exhibits high accuracy and robustness in fault classification, offering promising applications and value for widespread adoption.