Research on Seismic Performance Evaluation System of Power Grid Structure Based on Multi-Source Sensing Information

In this paper, the seismic performance evaluation system of power grid structure based on multi-source sensing information is studied, and a comprehensive evaluation method combining Kalman filter, principal Component Analysis (PCA) and support vector Machine (SVM) is proposed. By integrating the data in the sensor and using Kalman filter to fuse the multi-source sensor data, the noise is effectively reduced, and the reliability and accuracy of the data are improved. Then PCA is used to extract the main features in the data, which can reduce the computational complexity, improve the processing speed and efficiency of the model, and avoid the curse of dimensionality. Finally, SVM is used to classify and predict the seismic performance. SVM has excellent performance in classification and prediction, especially for high-dimensional data and small sample data, which can improve the accuracy of seismic performance evaluation and provide a scientific basis for the safe operation of power grid.