Accordingly, an important contributing factor to wind turbine lifespan is leading edge erosion (LEE), which decreases blade performance and longevity, increases maintenance costs, and causes reductions in annual energy production (AEP). Blades contribute at least 20% of the overall cost of wind turbines and are also a major source of failures and maintenance costs. The CNN is more successful at identifying shallow damage and exhibits better performance when applied to the images after they are preprocessed to a common blade orientation.īlade integrity is a fundamental determinant of power generation. Both models successfully identify approximately 65% of total damage area in the independent images, and both perform better at quantifying deep damage. Each model (CNN v PTS) is applied to quantify the percent area of the visible blade that is damaged and classifies the damage into deep or shallow using only the images as input. The images sample across a range of blade orientation, aspect ratio, lighting and resolution. The models are developed and tested using a dataset of 140 field images. The unsupervised approach aggregates pixel intensity thresholding with calculation of pixel-by-pixel shadow ratio (PTS) to independently identify features within images. The supervised ML model employs convolutional neural networks (CNN) and learns features (specific types of damage) present in an annotated set of training images. Two machine learning (ML) models are developed and evaluated for automated quantification of the areal extent, morphology and nature (deep, shallow) of damage from field images. Wind turbine blade leading edge erosion is a major source of power production loss and early detection benefits optimization of repair strategies.
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