Machine Learning for Single-Axis Tracker Fault Detection and Classification

Taos Transue, Marios Theristis, Daniel M. Riley

Abstract:

More than 90\% of utility-scale photovoltaic (PV) power plants in the US use single-axis trackers (SATs) due to their potential for substantially higher power production over fixed-array systems.
However, they are subject to software misconfigurations and mechanical failures, leading to suboptimal tracking accuracy.
If failures are left undetected, the overall power yield of the PV power plant is reduced significantly.
Robust detection and diagnosis of SAT faults is needed to minimize downtime and ensure continuous and efficient operation.
This work presents analytic tools based on machine learning to detect deviations in SAT tracking performance and classify SAT faults.