For power system disaster prevention and mitigation, risk assessment and visualization under typhoon disaster have important scientific significance and engineering value. However, current studies have problems such as incomplete factors, strong subjectivity, complicated calculations, and so on. Therefore, a novel risk assessment and its visualization system consisting of a data layer, knowledge extraction layer, and visualization layer on power towers under typhoon disaster are proposed. On the data layer, a spatial multi-source heterogeneous information database is built based on equipment operation information, meteorological information, and geographic information. On the knowledge extraction layer, six intelligent risk prediction models are established based on machine learning algorithms by hyperparameter optimization. Then the relative optimal model is selected by comparing five evaluation indicators, and the combined model consisting of five relatively superior models is established by goodness of fit method with unequal weight. On the visualization layer, the predicted results are visualized with accuracy of $1\mathrm{km}\times 1\mathrm{km}$ by ArcGIS 10.4. In results, the power tower damage risk assessment is carried out in a Chinese coastal city under the typhoon ‘Mujigae’. By comparing predicted distribution and similarity indicator of the combined model with those of the other models, it is shown that the combined model is superior not only in quality but also in quantity.

Typhoons can have disastrous effects on power systems. They may lead to a large number of power outages for distribution network users. Therefore, this paper establishes a model to predict the power outage quantity of distribution network users under a typhoon disaster. Firstly, twenty-six explanatory variables (called global variables) covering meteorological factors, geographical factors, and power grid factors are considered as the input variables. On this basis, the correlation between each explanatory variable and response variable is analyzed. Secondly, we established a global variable model to predict the power outage quantity of distribution network users based on Random Forest (RF) algorithm. Then the importance of each explanatory variable is mined to extract the most important variables. To reduce the complexity of the model and ease the burden of data collection, eight variables are eventually selected as important variables. Afterward, we predict the power outage quantity of distribution network users again using the eight important variables. Thirdly, we compare the prediction accuracy of a model called the No-model that has been used before, Linear Regression (LR), Support Vector Regression (SVR), Decision Tree Regression (DTR), RF-global variable model, and RF-important variable model. Simulation results show that the RF-important variable model proposed in this paper has a better effect. Since fewer variables can save prediction time and make the model simplified, it is recommended to use the RF-important variable model.