Economic evaluation of IOT–based maintenance in offshore wind energy: a case study in Taiwan

Due to an inexhaustible nature, offshore wind energy has become a potential alternative for many countries to overcome the anthropogenic negative impacts on the environment of fossil fuels. Offshore wind farms are usually positioned in an open space far away from the seashore where the wind is strong enough to generate electricity effectively and reliably. However, due to location characteristic, operation and maintenance are hard–to–reach and cause high cost. To address the needs of the fast growth of offshore wind energy, it is critical to analyse and estimate the maintenance planning due to its influence on life cycle cost, energy generation performance and system cost–effectiveness. In recent years, the Internet of things has extended to developments and become competitive trends in predictive maintenance technology. There is a large potential to use the Internet of Things in maintenance activities to make offshore wind energy more cost–effective. In this paper, a predictive maintenance model using the Internet of Things is considered and discussed. Moreover, this paper proposes an approach to analyse the cost–benefit of the using Internet of Things in offshore wind turbines maintenance through analysis of the failure rate with a probabilistic method. In addition, the estimation of energy generation under the real weather in Taiwan is integrated in the cost–benefit model for reliability. Based on the estimation model, it has been found that there is the potential toward increasing the attractiveness of offshore wind energy by using the Internet of Things in maintenance activities.