A unified analytical framework for capacity factor estimation and availability modeling in wind turbines: Bridging design and reliability

Abstract

This study presents a dual-framework approach for evaluating wind turbine performance, addressing both design-dependent capacity factor (CF) growth and operational reliability across the turbine lifecycle. Drawing from real-world fleet data between 2000 and 2021, a physics-based analytical model quantifies CF trends using key design parameters-hub height, rotor diameter, and rated power-incorporating vertical wind shear to estimate kinetic energy capture. The resulting kinetic energy to rated power ratio (KERPR) serves as a reliable proxy for CF, successfully tracking performance improvements over time. However, a persistent deviation between KERPR-based CF estimates and observed Effective Capacity Ratio (ECR) underscores the role of availability in shaping real-world outcomes. To explain this gap, the study introduces a theoretical construct grounded in repairable systems analysis, modeling availability variation with respect to turbine age through reliability and service support factors. Together, these models provide a robust, mutually validating toolset: one enabling rapid CF estimation for future turbine designs, the other offering a structured method to anticipate lifecycle performance. This integrated framework bridges the divide between theoretical capacity and practical operation, equipping stakeholders with clear, actionable insights to guide turbine design, comparison, and long-term optimization strategies.