Implementation of Power Grid Fault Diagnosis and Prediction Platform Based on Deep Learning Algorithms
Jianhui Wu · 2023
With the development of the power system, the issue of power grid operation safety is receiving more and more attention, deep learning algorithms can effectively solve the above problems. This article uses deep learning algorithms to preprocess, train, analyze fault data from different regions. By building visual technology based on deep classification models to achieve real-time diagnosis of power grid faults, this article designs the basic structure of the prediction platform. Afterwards, the performance of the platform was tested, the system test results showed that the feature extraction efficiency was over 80%, the recognition rate was over 90%, the visualization efficiency was over 91%, the system prediction time was 1-2 seconds. The network fault diagnosis and prediction platform requires a large amount of network data to train prediction deep learning algorithms. Research has shown that the accuracy of data quality is crucial for the effectiveness of the platform. Therefore, preprocessing steps are required to obtain a reliable and representative dataset. This article uses representation engineering domain knowledge technology to select features that have a significant impact on fault diagnosis prediction, and introduces them into deep learning models for prediction.