A Deep Unsupervised Learning Algorithm for Clustering of Wind Frequency Maps

Wind energy is now the world's second-fastest-growing electricity source. The power output of the wind farm depends on wind characteristics like wind speed and direction and wind farm layout. Specifically, these wind characteristics are modeled using a probability density function built using local wind measurements over the farm, called the Wind Frequency Maps (WFMs). The conventional approach for modeling this dynamic data is to perform manual feature extraction followed by static data clustering since the data is unlabeled. Nonetheless, since the features to be extracted are based on heuristics and may lead to information loss, this technique is inefficient. Thus, in this study, the wind characteristics data is treated in the form of images that are essentially the surface plots corresponding to the joint probability mass functions built over 12 direction sectors and 16-speed sectors. Moreover, the WFMs are modeled using a novel unsupervised Deep Learning framework where the required features are extracted using convolutional auto-encoders, followed by applying a soft clustering algorithm that can identify optimal cluster number. Here, 1400 such WFMs, were generated, 11 latent vectors were extracted, and finally, the images were grouped into 4 clusters with varying wind characteristics. Two of these clusters are found to be relatively denser. Further, this study will help perform wind farm layout optimization under uncertainty and control studies.