Heliostat calibration
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Solution Overview
Problem
Current heliostat calibration methods, such as the beam characterization system (BCS), are time-consuming and inefficient, requiring extensive calibration time for a field of heliostats, which leads to increased costs and less frequent calibration opportunities.
Innovation Solution
A method involving an imaging device positioned to capture images of a calibration target reflected by the heliostat, where multiple features of the target are identified, and a centroid of reflection is determined to calculate a normal vector for the heliostat, allowing for rapid and accurate calibration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the beam characterization system (BCS) is used for heliostat calibration, then calibration accuracy can be achieved, but calibration time becomes excessively long (170 hours for one calibration point for 5000 heliostats)
Solution Approach 1:
The calibration target is divided into multiple segments with different visual codes, allowing simultaneous calibration of multiple heliostats by having different subsets of segments visible to different heliostats. This segmentation enables parallel processing of calibration data across the heliostat field.
Solution Approach 2:
The imaging device is positioned in three-dimensional space above the heliostat field, capturing images from an elevated perspective. This spatial dimensionality change allows a single imaging device to simultaneously observe multiple calibration targets and multiple heliostats, dramatically increasing calibration throughput.
2Reliability
If heliostats are designed to be larger and more rigid to maintain accuracy without frequent calibration, then tracking accuracy can be maintained, but heliostat cost increases significantly
Solution Approach 1:
The system implements feedback by using the imaging device to capture images of calibration targets reflected by heliostats, then processing these images to determine reflection centroids and calculate normal vectors. This feedback loop enables frequent calibration of less rigid, cheaper heliostat structures, maintaining tracking accuracy without requiring expensive oversized components.
3Measurement precision
If more calibration points are collected to accurately determine heliostat tracking model parameters, then model accuracy improves, but calibration time increases to over a year (416 days)
Solution Approach 1:
Multiple calibration targets are pre-positioned at known locations throughout the heliostat field before calibration begins. The imaging device captures images of these pre-positioned targets reflected by multiple heliostats simultaneously, enabling rapid collection of numerous calibration points without sequential positioning operations.
Solution Approach 2:
The system merges multiple calibration operations into a single imaging capture event. By positioning multiple calibration targets and using a single imaging device to capture reflections from multiple heliostats simultaneously, the system combines what would otherwise be separate calibration procedures into one efficient operation, collecting many calibration points in parallel.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This method significantly reduces calibration time, enabling more frequent calibration of heliostats, which in turn allows for a less expensive and more robust heliostat structure, and facilitates quicker setup during the construction of CSP plants.
Implementation Method 1
A heliostat is a sun tracking mirror on a dual axis system that reflects sunlight onto a fixed spot
Implementation Method 2
positioning and orienting an imaging device so that a calibration target reflected by the heliostat is visible at the imaging device
Data Source
AI summary
Systems and methods for calibrating a heliostat (104) are disclosed. An imaging device (100) is positioned and oriented so that a calibration target (130) reflected by the heliostat (103) is visible at the imaging device and an image taken. Multiple features of the reflected calibration target in the image are identified and used to determine a centroid of reflection within the image which is then mapped to a corresponding centroid position on the calibration target. A vector t that extends between the centroid position on the calibration target and a known position of the heliostat, as well as a vectors that extends between the known positions of the imaging device and of the heliostat, are determined. A normal vector n of the heliostat is determined as the vector that bisects s and t and is used to calibrate the heliostat by updating parameters of a heliostat tracking model.