Modelling error chains in offshore wind energy systems: Examining the interplay of human performance and machine state

Abstract

A dynamic model of the mutual interdependency between humans and machines in offshore wind farms (OWFs) is developed in this paper. The model emphasises the importance of having early indicators to dynamically moderate human behaviour and the need to account for both human and physical subsystems and their mutual interactions for optimal asset management practices. This work simulates three distinct scenarios for: (1) examining the interconnectedness of technical and human dynamics and their implications on error and failure; (2) assessing the impact of production loss on human and organisational behaviour; and (3) evaluating human error probability as an early warning sign of production loss. The findings suggest that adopting an appropriate maintenance culture and considering human error likelihood and production rate in decision-making leads to optimising production and mitigating the risks associated with human error. The research highlights the significance of comprehending the complex interactions between human and machine factors in the operation and maintenance (O&M) of offshore wind turbines (OWTs). The proposed dynamic model helps organisations identify the underlying causes of errors, allowing them to improve their maintenance strategies.