Machine Learning-Based Prediction and Analysis Method for Substation Grounding Faults

A fault is a common and serious failure in an electrical system. Its occurrence not only affects the quality of the power supply but can also cause damage to electrical equipment and even safety accidents. Therefore, accurate prediction and timely handling of earthing failures are essential to ensure the stable operation of the electricity system. Traditional grounding fault detection methods rely on relay protection equipment and manual control, which have problems with detection delay and high false alarm rate and do not meet modern electrical systems’ real-time and accuracy requirements. Due to the development of smart grids and the widespread application of big data technology, machine learning-based error prediction methods have gradually become the hotspot of research. This study proposes a machine learning-based method for predicting and analyzing grounding failures in substations. By constructing three models: decision trees, random forests (RFs) and support vector machines (SVM) together with the operating data of the power system, accurate prediction of earthing failures is achieved. The test results show that the RF model performs well in all evaluation parameters, with the highest classification accuracy and generalization capability. Support vector machine models rank second, while decision tree models have relatively lower performance. To further improve the performance of the model, we balanced the data and optimized the parameters of the model using methods such as network search and random search. The accuracy of the improved RF model was 98.3% and 97.2% in training and testing sets, respectively, with AUC-ROC values of 0.994 and 0.981, which are significantly better than the other two models. This study verifies the effectiveness and feasibility of machine learning technology in predicting substation grounding failures and providing reliable technical support for detecting and preventing electrical system failures. Electricity companies are recommended to use RF models or other ensemble learning methods in practical applications with real-time monitoring and early warning systems to improve real-time error prediction and accuracy. Future research can further validate and optimize machine learning models, explore more advanced algorithms, improve the accuracy and strength of error prediction, and provide stronger technical support for smart grid development.