A Quantitative Framework for Diagnosing Energy Losses in Wind Turbine Operational Logs

A Single-Turbine Case Study

Abstract

Wind turbine operators routinely record daily generation alongside categorized downtime, yet this operational log data is typically reviewed only to confirm that a turbine generated electricity on a given day, without a systematic accounting of why its output fell short of potential. This study presents a reproducible framework for decomposing a wind turbine’s total available time into five categories (generating, grid-unavailable, mechanically broken down, under maintenance, and idle for insufficient wind), classifying the documented cause of lost time from free-text operational remarks, and statistically testing whether the resulting patterns in downtime timing, cause, and season are distinguishable from random variation. The framework is demonstrated on 1,614 consecutive days (1 April 2022 to 31 August 2026) of daily operational logs from a single 250 kW wind turbine generator in Tamil Nadu, India. Both mechanical breakdown and grid-related outages showed inter-event timing inconsistent with a homogeneous Poisson process (Monte Carlo-corrected p = 0.0005), suggesting temporal clustering, though the evidence is confounded by seasonality and does not, by itself, establish that individual events are causally dependent. The near-total drop in generation observed January through March each year was statistically large (Kruskal–Wallis ε² = 0.77) and coincided with the turbine being overwhelmingly classified as Lull rather than Grid-Down or Breakdown. Using month-specific generation rates rather than a single site-wide average reversed the ranking of the two largest documented causes by hours lost: Weather-related hours exceed Grid-infrastructure hours, but Grid-infrastructure losses are worth more in estimated annual revenue, because Weather losses concentrate in a lower-generation season. However, the single largest category of estimated generation opportunity loss overall was downtime with no recorded cause, exceeding any specific documented cause. The analysis pipeline, released as open-source, is potentially applicable to other turbines recording comparable fields, subject to validation of category definitions and data quality.