Abstract
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 km × 1 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.
Highlights
As one of the extreme disasters, typhoon has a tremendous impact on the power system
This paper proposes a risk assessment and its visualization method of power tower under typhoon
This paper proposes a risk assessment and its visualization method of power tower under disasters based on machine learning algorithms
Summary
As one of the extreme disasters, typhoon has a tremendous impact on the power system. Yin et al pointed out that most of the faults in typhoon. This layer is used to establish predicting models for damage probability prediction on the target side. Typical individual learning regression algorithms include LR (logistic regression) [24], SVR (support vector regression) [25], CART (classification and regression tree) [26,27], and so on. Typical ensemble learning regression algorithms include Adaboost iteration [28,29], GBRT (gradient regression tree) [30], RF (random forest) [31], and so on. The common binomial logistic regression model is defined by Equations (3) and (4)
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