AI Backtracking Single-Axis Tracking System

Product Overview

Conventional backtracking algorithms use a geometric model of the sun's position and array dimensions to calculate row angles that prevent inter-row shading. This works well on perfectly flat terrain with uniform rows — but real project sites have micro-topography variation, uneven row spacing from foundation installation tolerances, and diffuse light conditions that the geometric model cannot account for.

The AI Backtracking Single-Axis Tracking System replaces the geometric model with a machine-learning controller that continuously optimizes row angles based on real-time string-level electrical output data. Rather than calculating where shading should occur, the AI detects where shading is occurring and adjusts accordingly.

How it works:

Each row's DC string current is monitored at the combiner box level (no additional sensors required — the data is already present in the plant SCADA system)

During backtracking periods (early morning, late afternoon), the AI controller performs micro-adjustments to each row's angle

When a row's current drops relative to its neighbors — indicating shading — the controller adjusts that specific row, not all rows uniformly

Over time, the ML model learns the site-specific shading pattern and predicts optimal angles for given time/date/weather conditions, reducing the need for active micro-adjustments

Measured benefit: Shading losses reduced by up to 90% compared to geometric backtracking, translating to approximately 2–4% additional annual energy yield over a standard tracker with geometric backtracking — achieved with zero additional hardware cost beyond the controller firmware upgrade.

Technical Specifications

Applications

Sites with uneven terrain or micro-topography variation

High-density tracker layouts (GCR 0.45)

Projects where every 1% yield improvement translates to significant PPA revenue

Retrofittable to existing tracker installations (controller firmware upgrade only)

FAQ

Q. Does the AI controller require internet connectivity?

No. The ML model runs on an edge controller located in the plant's control room. The initial model training uses 2–4 weeks of on-site operational data to learn site-specific shading patterns. After training, the model runs locally. Periodic cloud connectivity is optional for remote firmware updates and performance benchmarking.

Q. Can this be retrofitted to existing tracker installations?

Yes, if the existing tracker hardware (slew drive, torque tube, foundation) is mechanically sound. The upgrade is a controller firmware swap and configuration of the string-level current data feed from the plant SCADA. No mechanical modifications are required.

Image Placement

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