Imaging Wind Turbine Fault Signatures Based on Power Curve and Self-Organizing Map for Image-Based Fault Diagnosis

Wind turbine fault diagnosis based on time-series data from the supervisory control and data acquisition system, remains a challenging task. Due to various stochastic effects, one of the main challenges includes the fact that, a change in a few data points is not necessarily an indication of a fault; hence, how to interpret time-series trends is often a major challenge. In this paper, we propose an innovative approach which aims at encoding power curve time-series data into imagery data using the self-organizing map. This approach enables one to expose fault signatures in terms of data topological structure for a subsequent image-based fault diagnosis purpose. The obtained experimental results based on data from a real operational wind turbine, indicate the effectiveness and impact of the proposed method, when used as a primary procedure for an image-based fault diagnosis system.