Research on Fault Diagnosis and Prediction Technology of Digital Power Grid Based on Big Data Analysis

With the increasing complexity and scale of power system, traditional fault diagnosis and prediction methods are facing many challenges. Therefore, this study uses big data analysis technology to mine hidden information and patterns in massive operation data to achieve more accurate fault diagnosis and prediction. Firstly, this study uses data mining technology to extract fault-related features from complex power grid operation data. Secondly, support vector machine (SVM) and random forest (RF) supervised learning algorithm are used for fault diagnosis. In addition, by integrating several basic learners (SVM and RF), the final fault diagnosis and prediction results are obtained by voting. This integrated learning strategy can improve the generalization ability and stability of the model. The experimental results show that the fault diagnosis and prediction model based on ensemble learning proposed in this study is superior to other commonly used methods in accuracy, recall and F1 score. Especially when dealing with complex and uncertain power grid faults, this method shows higher robustness and accuracy. This study not only puts forward an effective fault diagnosis and prediction method for digital power grid, but also overcomes the limitations of traditional methods through big data analysis technology, which provides strong support for the stable operation of power grid system.