Predictive maintenance for solar fleets: inverters, trackers and strings

Faults that build slowly enough to be seen in the data, across four equipment groups:

• Inverters: rising internal temperatures, cooling fan problems, repeated trips and derating that alarms treat as one-off events.
• Tracker motors and controllers: rows that lag their neighbors, stall at the same angle, miss stow or draw unusual current.
• Strings: output below neighboring strings under the same sun, pointing to a connector, fuse, module or wiring issue.
• Soiling: losses that grow between rains or washes, separated from weather so cleaning is scheduled when it is worth it.

Because each source alone raises false alarms. Inverter portals report what the inverter thinks, SCADA records site power and meter data, and weather explains most of the variation in between. Lining them up by time and equipment, with on-site pyranometer readings checked against satellite irradiance, lets a model tell lost energy from low sun.

Much of this is data engineering. Tag names differ across sites and vendors, clocks drift and communication gaps leave holes, so the first phase usually builds a clean data layer, the work described under data infrastructure. Every string or tracker row is then judged against its neighbors and against expected output for the measured irradiance and temperature.

It turns a list of alarms into a ranked work plan per site. A technician driving out to a remote site should replace the failing fan, reset the stalled tracker controller and check the weak strings on one visit, and the plan says which parts and tools to bring.

Flags become work orders in your CMMS with the evidence attached, so planners can weigh expected lost energy against the cost of a visit. Findings from the field are recorded against each flag, which is how the models learn your fleet. The solar and renewables industry page covers our wider work for operators.