Accurate fatigue damage estimation is a critical step for managing and extending the life of wind turbine fleets, and the basis on which operators decide whether an ageing asset can be kept in service beyond the end of design life. The most accurate estimation relies on continuous high-frequency strain measurements processed with rainflow cycle counting. However, the data volumes, computational complexity, and associated costs of doing this over long-term periods and across large fleets make it impractical at fleet scale. For this reason, industry practice has long relied on stress data aggregated over 10-minute windows already used by Supervisory Control and Data Acquisition (SCADA) systems. This choice is convenient but tends to underestimate fatigue damage generated by cycles whose period exceeds the windows, driven by slow variations in wind speed, operational transitions and control actions.</p> <p>Technology has recently enabled operators to deploy second‑resolution SCADA metrics at scale and at reasonable cost. This is an opportunity to revisit fatigue monitoring at a SCADA‑aligned timescale that is short enough to retain the cycles a 10‑minute window discards, yet aggregated enough to remain deployable across large fleets. This paper benchmarks the families of methods that make this opportunity actionable against continuous rainflow as a baseline: conventional window‑based counting, a modified Low‑Frequency Fatigue Dynamics (LFFD) formulation that explicitly resolves intra‑window and inter‑window damage contributions, and two reduced‑information extrema‑sequence representations — Start-Peaks-Valleys-End (SPVE) and Maximum and Minimum (Max–Min). The evaluation uses six months of 15‑second windows of SCADA and strain measurements acquired at 50 Hz from two instrumented tower sections of an onshore wind turbine and examines the influence of window length, sampling rate, and fatigue‑curve formulation on damage computation (Eurocode 3 <em>vs</em> DNV).</p> <p>Reducing the aggregation window from 10-minute to 15-second alone does not improve fatigue estimation, as shorter windows increasingly truncate the low-frequency cycles that dominate accumulated damage. The proposed modified LFFD framework overcomes this limitation by preserving cycle continuity across window boundaries, recovering 99.5 % of the baseline damage while requiring only a fraction of the original data volume. Reduced-information representations further demonstrate that fatigue can be estimated with high accuracy using strongly compressed stress sequences, with Start-Peaks-Valleys-End (SPVE) and Maximum and Minimum (Max–Min) retaining 98.8 % and 96.2 % of the baseline damage, respectively. These findings establish a practical pathway towards SCADA-aligned, data-efficient fatigue monitoring, enabling scalable lifetime assessment across large wind turbine fleets while preserving the fidelity of high-resolution structural measurements.