Digital Twin of Wind Generator to Simulate Different Turbine Characteristics Using IoT

Abstract

The Internet of Things has emerged as one of the most important components for fault diagnostics in industrial applications. The emergence of the digital twin has further enhanced the capability to develop and implement industrial applications more easily to simulate industrial environments. This article describes an alternate method to generate data using a digital twin of the wind generator, which can be utilized for data collection and fault diagnostics using trained predictive models. The model of the wind turbine can provide valuable information for monitoring, controlling, and predicting the wind generator’s behavior. This also helps to reduce costs for shutdowns and scheduled maintenance as the maintenance can be moved to on-demand. It also provides a platform to optimize and test out new models for wind generators before implementing them in the field. The test bench presented in the article enables implementation of different design curves for wind generators to generate data for different faults and scenarios using a servomotor. This can help in optimizing the design for wind farms that can support wind energy generation efficiently.