Lightning risk assessment of offshore wind farms by semi-supervised learning

Abstract

The wind turbine has rapidly developed worldwide with increasing height and scale, resulting in the increased risk of lightning strikes. When wind turbines were stroke by the lightning, they will be damaged, causing economic loss and outage. Lightning risk assessment can guide the improvement of lightning protection and the design of the wind farms to efficiently prevent lightning damages. The traditional lightning risk assessment methods rely on subjective features to some extent. The existing lightning risk assessment methods based on machine learning demand abundant labeled data. It is extremely difficult to label and acquire the data. This paper proposed a lightning risk assessment method based on semi-supervised learning to address the challenges of labeling negative samples and limited labeled data. The semi-supervised K-means algorithm is proposed to divide all data into three parts. The Laplacian support vector machine (LapSVM) with hyperparameters optimized by the particle swarm optimization (PSO) is used to assess the lightning risk. The proposed method has better performance than the standard SVM and neural network (NN). Moreover, previous researches did not consider lightning protection ability. This paper introduces the receptor number into lightning risk assessment. The assessment results suggest that there is higher lightning risk in areas with a great number of wind turbines, high lightning density, and strong lightning strength. The ocean area is more likely to have low lightning risk. The results are valuable for lightning protection optimization of existing wind farms and can give guidance for the plan of new wind farms.