Wind Turbine Data-Driven Intelligent Fault Detection

Accurate forecasting of wind power is essential for preserving the stability of the system, reducing costs, and making the most of available resources. On the other hand, there is a limited amount of data to work with, which, along with the high processing needs, makes the training process resource-intensive. The purpose of this research is to offer a deep learning model that relies on a data-driven approach and is aimed to increase prediction accuracy while simultaneously minimizing the expenses of training. Our method effectively leverages wind power data from comparable situations or turbines, so decreasing the need to gather more data and thereby simplifying the creation of a more accurate wind power prediction model with little target data. This is accomplished by successfully leveraging wind power data from analogous scenarios or turbines. For the purpose of pre-training the model, the suggested approach makes use of the complex properties included within previously collected data on wind turbines that are analogous to those presented, hence lowering the likelihood of the model being overfit. After the models have been pre-trained, they are then used to infer the behaviour of additional turbines in order to provide predictions about the future wind power output for those turbines. The results of the simulations show that our model is superior to other deep learning models in terms of the accuracy of its predictions and the efficiency with which it uses time when compared to those other models.