Analysis and Evaluation of Backtracking Settings for Energy Yield Optimization at the Cal Poly Solar Farm

Author(s) Information

Kayla Go-Oco, California Polytechnic State University, San Luis ObispoFollow

College - Author 1

College of Engineering

Department - Author 1

Electrical Engineering Department

Advisor

Dale Dolan, College of Engineering, Electrical Engineering Department

Funding Source

Cal Poly’s Noyce School of Applied Computing.

Date

10-2025

Abstract/Summary

The Cal Poly Solar farm has been built as a single axis tracking facility with two different types of panels. Both conventional single cell solar panels and twin cell solar cells have been used in its construction. Twin Cell panels typically perform better than their conventional counterparts when shaded by other panels in the row in front of them in a fixed tilt system. However neither module performs well when even a small portion is shaded. This project will access the system API to access data for the field and process to determine the energy yield improvements that can be made by optimizing backtracking settings for each array in the Cal Poly Solar Farm. Backtracking is a technique where the single axis tracker avoids shading other rows by sub-optimally tracking the sun until it is high enough in the sky that this is not necessary. This is relatively straightforward in a field that is flat or at least has all controlled modules in the same array. However the Cal Poly Solar farm installation has significant variation in terrain and this was transferred to the modules in adjacent rows such that they are not in the same plane nor are they parallel. As a result there is significant error in the backtracking algorithm that was not designed for this case which is seen by significant power losses in both of the types of panels. The subject of this research project is to determine the improvements that can be made in energy yield by adjustment of the backtracking settings for each array. The parameters have been adjusted and we will need to determine the change in daily energy yield that is achieved for all 12 trackers. This is extremely time intensive and we will be using automation to implement the calculations and comparisons, likely through use of Python. I have obtained access to allow data acquisition for power and energy data and operation of the tracking system at the Cal Poly Goldtree Solar Farm to allow this project to be completed 100% remotely if necessary. All control and data collection can be accomplished via a web based dashboard that allows us to monitor the performance of the farm in real time and to collect data that can be analyzed to determine the improvements.

October 1, 2025.