Solar Array Zonal Tracker Control for Cloud Cover Optimization
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Solution Overview
Problem
Large solar arrays face reduced energy production due to lack of granularity in solar tracker movement, as not all trackers adjust to optimal diffuse light positions, especially in large arrays where cloud cover disparities significantly impact output.
Innovation Solution
A method for controlling solar trackers by defining zones within the array based on light conditions, using current and voltage data, satellite imagery, and weather forecasts to determine zone-specific angles, and continuously updating these zones as cloud cover changes, allowing for precise orientation of solar trackers to maximize energy capture.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If all solar trackers are controlled uniformly across the entire array, then the control system is simple and easy to operate, but energy production is reduced because trackers in different cloud cover conditions cannot be optimized individually
Solution Approach 1:
The solar array is divided into multiple zones based on cloud cover conditions detected by sensors. Each zone can be independently controlled with zone-specific tracker angles, allowing trackers in cloudy zones to optimize for diffuse light while trackers in clear zones continue normal sun tracking. This segmentation resolves the contradiction by enabling differentiated control that improves overall energy production without requiring complete system redesign.
Solution Approach 2:
Different regions of the solar array are assigned different operational characteristics based on local cloud cover conditions. Trackers in each zone receive customized control parameters (diffuse fraction irradiance thresholds, target angles) appropriate to their specific environmental conditions. This local quality approach allows the system to maximize energy capture in cloudy regions while maintaining optimal performance in clear regions, resolving the productivity-complexity contradiction.
2Productivity
If solar trackers are placed in diffuse light position to maximize energy capture under cloud cover, then energy production increases in cloudy zones, but the control and measurement systems become more complex
Solution Approach 1:
Diffuse fraction irradiance (DFI) sensors are deployed as intermediary measurement devices to detect cloud cover conditions. These sensors provide quantitative data about the proportion of diffuse versus direct solar radiation, enabling the control system to objectively determine when trackers should switch to diffuse light positioning. This intermediary measurement approach simplifies the detection task compared to complex image analysis while providing sufficient information for optimal control decisions.
Solution Approach 2:
The system continuously monitors DFI values from sensors and uses this feedback to dynamically adjust tracker positioning. When DFI exceeds predetermined thresholds, the control system automatically switches trackers to diffuse light position; when thresholds are not met, normal sun tracking is maintained. This feedback mechanism enables automatic adaptation to changing cloud conditions without requiring complex real-time analysis, resolving the contradiction between improved energy capture and measurement complexity.
3Productivity
If zone definitions are continuously updated as cloud cover changes, then energy optimization is maximized in real-time, but computational requirements and system complexity increase
Solution Approach 1:
The system updates zone definitions and control parameters at regular time intervals rather than continuously in real-time. At each update cycle, current DFI measurements are used to reassign trackers to zones and recalculate optimal angles. This periodic approach balances real-time optimization needs with computational efficiency, avoiding the excessive processing requirements of continuous updates while still capturing most of the potential energy gains from dynamic cloud cover changes.
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 approach enhances energy production by ensuring that solar trackers are optimally positioned in real-time, accounting for cloud cover and shading, thereby increasing the overall yield of the solar array, particularly in large sites where cloud cover variations are frequent and impactful.
Implementation Method 1
receiving current and voltage data from a plurality of solar modules of the solar array
Implementation Method 2
using a combination of sensors, satellite imagery, and other data sources to determine the light conditions
Data Source
AI summary
A method of controlling a solar array including receiving current and voltage data from a plurality of solar modules of the solar array, calculating a diffuse fraction irradiance for the plurality of solar modules, mapping the diffuse fraction irradiance for the plurality of solar modules, generating a digital image of light conditions in the solar array based on the mapped diffuse fraction irradiance, defining zones within the array based on the light conditions in the digital image, determining a zone-specific solar tracker angle for each zone based on mapped diffuse fraction irradiance, transmitting the zone-specific solar tracker angle to a computing device associated with each solar tracker in the solar array, and driving the solar trackers of each zone such that the solar trackers that make up each zone are oriented to substantially the same angle.