Convolutional Neural Network for Wind Turbine Failure Classification Based on SCADA Data

Abstract

As a renewable energy source and an alternative to fossil fuels, the wind power industry is growing rapidly. However, due to harsh weather conditions, wind turbines (WT) still face many failures that raise the price of energy produced and reduce the reliability of wind energy. Hence, the use of reliable monitoring and diagnostic systems of WTs is of great importance. Operation and maintenance expenses represent 30% of the total cost of large wind farms. The installation of offshore and remote wind farms has increased the need for efficient fault detection and condition monitoring systems. In this work, without using specific custom devices for monitoring conditions, but only increasing the sampling frequency in the sensors already available (in all commercial WT) of the supervisory control and data acquisition system (SCADA), datadriven multiple fault detection is performed, and a classification strategy is developed. The data is processed, and subsequently, using a convolutional neural network (CNN), six faults are classified and evaluated with different metrics. Finally, it should be noted that the classification speed allows the implementation of this strategy to monitor conditions online in real under-production WTs.