Tracking the Truth: A Data-Centric Evaluation of Solar Tracker Underperformance in Utility-Scale PV Plants

The global solar tracker market is on a steep growth trajectory, projected to expand from USD 8.67 billion in 2024 to USD 25.24 billion by 2032, at a CAGR of 14.3%. North America—particularly the U.S.—is leading this surge, with the U.S. market alone expected to reach USD 9.58 billion by 2032.
As utility-scale solar portfolios scale into the gigawatt (GW) range, so does the pressure to meet modeled performance and financial expectations. While solar trackers offer theoretical advantages over fixed-tilt systems, many asset owners continue to fall short of their energy yield targets. Common operational issues such as tracker misalignment, actuator or battery failures, and sun-angle stalling frequently go undetected or unquantified, resulting in significant energy and revenue losses. These challenges are compounded by the massive volumes of data generated across large-scale portfolios—data that is often underutilized.
This session presents a data-centric approach to solar tracker performance analytics at scale, leveraging an Asset Performance Management (APM) platform to monitor, diagnose, and optimize tracker behavior across GW-scale assets. By integrating high-resolution telemetry, SCADA data, and machine learning models, we demonstrate how asset operators can systematically detect underperformance, localize faults, and quantify their financial impact—empowering data-driven decisions that maximize generation and ROI.