Dynamic Reliability Assessment Model for IoT-Enabled Smart Offshore Wind Farm

Offshore wind farm is one of the most promising applications in the Internet of Things (IoT), due to being energy-renewable and resources-unlimited. However, the reliability monitoring and maintenance models of power equipment based on communication paths and sensors are still immature in the smart offshore wind farm (SOWF). Based on the hierarchical architecture and end-to-end communication, a dynamic reliability assessment model (DRAM) is proposed for SOWFs. First, based on the IoT hierarchy, a four-stage network is developed to represent the relationship or dependencies between diverse devices in a complex SOWF. Second, a two-layer DRAM with forward monitoring (FM) and lateral protection (LP) is proposed. The FM encompasses a sensor network-based state-monitoring phase (monitoring weather data like temperature and wind speed), and a data-monitoring phase (monitoring the reliability-related data like reception power and data processing speed). The LP includes a signal-protection mode (LP-I) ensuring that virtual machines read the data and issue protection orders before turbine failures to minimize losses, and a radius-maintenance model (LP-II) performing maintenance of the failed turbine nodes. Simulation results show that the optimal maintenance strategy based on DRAM outperforms the benchmark maintenance method for traditional wind grids.