Session: 18-01 HelioCon Metrology

Paper Number: 142413

142413 - A Non-Intrusive Optical (Nio) Approach to Characterize In-Situ Optical Performance of Heliostats: Field Testing and Tracking Error Estimates 

Abstract: 

The performance of power tower concentrating solar power (CSP) plants is contingent on the performance of the heliostat field. Surface level deformations, errors in facet canting, and errors in heliostat pointing, called tracking errors, can cause dramatic losses to the energy reaching the receiver.

Several proposed methods use heliostat-affixed sensors, specially designed targets, or laser-based alignments, but most are designed to address overall errors in pointing only. The most commercially prevalent calibration system is the Beam Characterization System (BCS), which requires heliostats to be taken off target, and outputs tracking errors only. A full BCS calibration can take several months to over a year.

 

The Non-Intrusive Optical (NIO) method is designed to characterize these sources of error in the solar field without interrupting the plant’s performance. With a patent for the approach and several field test campaigns, the technology continues to march towards a software product to assist CSP plants in understanding their solar fields and their options for improvement.

 

In this presentation, we will address the testing campaigns conducted to date, the progress these test campaigns produced, the current status of the technology, and a comparison of the tracking error estimates derived from NIO versus the tracking errors derived from commercially available systems. We will also present the progress on NREL’s own BCS system, which we endeavor to validate in-situ heliostat measurement systems on.

 

Test Campaign 1 – May 2021

 

This weeklong test campaign was the initial field test of the NIO flight planning algorithm and post-processing on a commercial heliostat. Previous tests had been conducted at NSTTF, but the live field test offered insights into long-range heliostat measurement and first attempts at measuring tracking heliostats.

 

Heliostats were measured in fixed orientations in several locations in the field. Technical difficulties limited the volume of data collected, but not the value. Strong datasets on a small number of heliostats were collected.

Test Campaign 2 – October 2021

The test campaign in October of 2021 sought to test larger numbers of heliostats with consumer-level aircraft, but yielded many manual, experimental flights to constrain the data collection process and guide software updates. The importance of knowing a priori the heliostat aimpoints to some degree of accuracy was highlighted here, as the standby aimpoints were uncertain and injected this uncertainty into the data collection process.

Test Campaign 3 – May 2022

The May 2022 campaign was a targeted implementation of the NIO method to cover large numbers of actively tracking heliostats. Overall, the NREL team averaged 10 flights per day, utilizing the automatic path planning to achieve up to 42 heliostats per flight on a small Parrot ANAFI. The ANAFI can stay aloft for approximately 21 minutes.

Technology Status:

The technology is under validation testing and refactoring for commercialization. In this presentation, we will show the first validation results of a tracking heliostat computed by NIO and data collected via BCS for the same heliostat, as well as amalgamated metrics for the data post-processing steps. We will briefly discuss the overall NIO architecture and where it fits into the current industry environment with insights Rebecca Mitchell and Tucker Farrell have gained from Energy I-Corps.

 

Further validation and testing of the NIO alpha version will be conducted on NREL’s own BCS. We will present the design models and construction photos of the NREL BCS, the future site, and its place in the heliostat measurement process.

Presenting Author: Tucker Farrell National Renewable Energy Laboratory

Presenting Author Biography: Tucker has been a researcher at NREL for just over 4 years. After earning his MS in aerospace engineering focused on autonomous controls, estimation, and decision-making, he started his research career at NREL developing the path-planning algorithm for the NIO method. While never fully letting go of the path-planning aspect, he picked up the image-processing tasks after the first test campaign to automate as much of the process as possible. He continues to work to improve these functions while moving the technology towards an alpha version suitable for end-user testing. At the lab, he's also involved in running the solar furnace and conducting CSP materials research.

Authors:

Tucker Farrell National Renewable Energy Laboratory
Rebecca Mitchell NREL
Kyle Sperber NREL
Guangdong Zhu NREL