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As the scale and complexity of power grid continue to increase, some abnormal events pose a significant threat to the security and stability of the power grid. This paper presents a novel approach based on the Random Forest algorithm for the substation fault detection. The approach is designed to improve the accuracy and efficiency of fault detection of substation equipment. By utilizing the machine learning techniques, specifically taking the decision tree algorithm as the base model, and following the principles of ensemble learning, a Random Forest algorithm is designed from multiple decision trees. The bagging technique is employed to create an ensemble learning model. The data preprocessing phase includes some crucial steps such as feature selection, interpolation preprocessing, normalization preprocessing, and the creations of training and testing sets. Additionally, appropriate evaluation metrics are chosen to assess the performance of our proposed approach. The experiments conducted on substation fault data collected from Suzhou City from January 2023 to August 2023 demonstrate the effectiveness of the approach. The dataset comprises six monitoring signals regarding the substations, with labels indicating the respective types of substation faults. The experiment results indicate that our proposed Random Forest-based approach achieves a extremely high accuracy rate compared to other baseline approaches. This approach can also help to enhance the substation fault detection capabilities and ensure the reliability and stability of power grid system.
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