Assessing wind turbine lightning risk using data-driven methodology

Abstract

This paper presents a methodology to conduct a risk assessment of lightning damage for wind farms using a data-driven methodology. Methods exist to address risks related to fire but this approach goes further to address lightning damage and related impacts to operations and maintenance comprehensively - monetizing the costs of these impacts. The process begins with lightning data analysis and strike rate calculation. Then, the lightning damage data is analyzed to gain insight into the repair cost and impact. Lastly, the process-based cost model takes lightning strike rate and impact data, resulting in risk provision. The paper presents two case studies, one for onshore wind farms and one for offshore wind farms. The relative importance of operations and maintenance costs versus lost revenues due to downtime (liquidated damages) differ between the two cases due to underlying technical drivers (turbine sizes, wind farm performance, lightning prevalence) as well as market drivers (labor costs, electricity prices). Thus, the study demonstrates both the viability of the method in providing monetary estimates of lightning damage costs over the lifteime of a wind farm as well as the importance of site-specific analysis and models to ensure accurate estimation of those costs.