A Fog Computing Based Approach Towards Improving Asset Management and Performance of Wind Turbine Plants Using Digital Twins

Wind turbines are one of the sustainable means of renewable energy which have a life span of many years. Despite their durability, there is a need for maintenance to prevent downtime. With industry 4.0, wind turbines are usually implemented with smart technologies that connect them to base stations enabled with information systems for management. The implementation architecture usually adopted for this is centralized and does not factor in the need for intelligence at the edge of the network where minute detail of defects can be observed in real-time. This centralized approach also increases the cost of computation and latency. Overall, this indicates a negative effect on the efficiency of wind turbine asset management. This paper proposes the use of Fog computing architecture to implement a system of monitoring the unit and system level operations of the wind turbine plant using digital twins. The proposed concept attempts to improve the performance of the wind turbines by using decentralized intelligence at the fog nodes, where sensor data is pre-processed before sending it to the global digital twin in the cloud for deeper insights.