Risk Visualization of Power Tower under Typhoon Disaster Based on Multi-source Heterogeneous Information

Abstract

Risk visualization of power system under typhoon disaster has important scientific significance and engineering application value for power system disaster prevention and mitigation. In this paper, based on the multi-source hetero-generous information database, such as equipment operation information, meteorological information and geographic information, the damage probability models of the main power tower network based on 6 machine learning algorithms including AdaBoost iteration algorithm, GBRT (Gradient Boost Regression Tree), RF (Random Forest), LR (Logistic Regression), SVR (Support Vector Regression) and CART (Classification and Regression Tree) are established by utilizing the historical damage data of the main power tower network under typhoon disaster in a coastal city, and the error and prediction accuracy of the models are compared. Then by combining with the historical data of typhoon “Mujigae”, the predicted damage probability and risk value of each model are visualized with the geographic grid of $\mathbf{0.15}^{\circ}\times \mathbf{0.1}^{\circ}$ . The predicted effectiveness of these models is compared, and the ideal model and display index is selected.