Research on Power Equipment Fault Diagnosis and Prediction Model Based on Data Clustering

This article advocates a power device anomaly detection and prediction architecture based on data classification, with the goal of improving the robustness of power device operation and the accuracy of abnormality predictions. At the beginning of the research, data classification technology was used to classify and analyze massive power device operating data to identify the normal and abnormal conditions of the device. This paper adopts the isolation forest algorithm to enhance the identification performance of isolated points and identify hidden abnormal patterns more deeply. The isolation forest algorithm is particularly suitable for operating high-dimensional and non-linear data, and can efficiently cope with the complex operation scenarios of power equipment. In order to confirm the practicality of the architecture, this article uses actual power device operating data to conduct simulation verification, and educates and confirms the architecture. Verification results show that the accuracy of this architecture in the field of anomaly detection and prediction exceeds $\mathbf{9 5 \%}$, and the false alarm rate is significantly reduced. Through this architecture that combines classification and isolation forests, complex power data can be processed, and the accuracy and reliability of power device anomaly detection and prediction can be significantly improved.