Posters

Abstract

The integration of diverse sensor datasets, including SCADA, vibration, and meteorological sensors, is pivotal for advancing wind turbine performance. This paper presents an exploration of the challenges and opportunities of combine such data for optimising turbine efficiency and reliability. We address the complexities of combining multimodal datasets, which vary in scale, dimensionality, and temporal resolution. A focus is placed on innovative data fusion techniques that yield a holistic understanding of turbine operations under varying conditions. Key challenges in this integration process, such as managing large data volumes, ensuring data integrity, and resolving interoperability issues, are critically examined. We then pivot to the substantial opportunities this integrated approach presents. These include enhanced predictive maintenance capabilities, more accurate fault detection, and optimised energy production. The potential of advanced analytics, particularly machine learning and AI, is explored for turbine performance improvements. Our analysis provides a comprehensive overview of existing integrating multimodal sensor data, highlighting advantages and disadvantages.