James Carroll is a specialist in wind turbine reliability engineering and data-driven analysis of offshore wind systems. His research is built on the use of engineering data analytics, applied statistics and large-scale operational datasets, with a focus on extracting reliable insight from real-world wind turbine data.

A central aspect of his work is the quantitative analysis of wind turbine failure behaviour. He has extensive experience working with industrial datasets including SCADA data, vibration measurements, work orders and failure logs, often spanning thousands of turbines and multi-year operating periods. Statistical failure-rate modelling and lifetime analysis are used to establish robust benchmarks for turbine and sub-assembly reliability, particularly in offshore environments.

This analytical capability underpins his work on offshore operation and maintenance. By combining failure frequency, repair severity and time-dependent behaviour, his analyses support availability modelling, maintenance planning, spare-parts logistics and cost-of-energy assessment. His work highlights how different subsystems evolve through time, distinguishing early-life, steady-state and wear-out failure mechanisms.

Building on this foundation, James has carried out large-scale empirical comparisons of wind turbine drivetrain architectures. Using statistically homogeneous turbine populations, he has compared doubly-fed induction generator systems with permanent-magnet generator systems, analysing generator and converter failures, repair severity and year-on-year trends. These studies provide evidence-based insight into reliability trade-offs, particularly relevant for offshore deployment where access and repair costs are dominant.

In parallel, his work in condition monitoring and predictive maintenance applies machine learning techniques to high-volume SCADA and vibration data. This includes the prediction of gearbox failures and estimation of remaining useful life, supporting predictive maintenance strategies and the development of advanced monitoring systems.

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Key expertise

• Reliability analysis using SCADA data, failure logs and repair records
• Statistical failure-rate modelling and lifetime assessment of wind turbine subsystems
• Offshore O&M cost and availability modelling for maintenance decision support
• Empirical comparison of drivetrain architectures using operational fleet data
• Predictive maintenance and data-driven estimation of remaining useful life

His work contributes to WECC’s capability in reliability-led wind energy research, supporting turbine design decisions, operational strategy and technology de-risking from concept through deployment.