Solar Panel Row Tracking for Topography-Aware Energy Capture
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Solution Overview
Problem
Solar tracking systems are inefficient when weather conditions change or do not account for local topographies, leading to reduced light capture and energy conversion efficiency.
Innovation Solution
A solar tracking system with multiple rows of solar panel modules that can be oriented independently based on a performance model predicting energy output from weather conditions and irradiance ratios, using a mesh network for fail-safe functionality and periodic updates with learning algorithms to optimize energy capture.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If solar tracking systems use a single unified orientation for all rows, then device complexity is reduced, but energy output is reduced due to inability to account for local topographies and weather variations
Solution Approach 1:
The solar panel array is divided into multiple independently controllable rows, each capable of being oriented separately based on local conditions. This segmentation allows each row to optimize its orientation for maximum energy capture while accounting for local topographies and weather variations, rather than forcing a single unified orientation for the entire array.
Solution Approach 2:
Each row of solar panels is given the capability to have its own local orientation control based on site-specific data and local weather conditions. The system applies different orientation strategies to different rows depending on their specific topographic context and immediate environmental conditions, rather than applying a uniform control approach across all rows.
2Productivity
If solar tracking systems account for local topographies and weather conditions, then energy capture efficiency is improved, but device complexity increases due to multiple sensors and control mechanisms
Solution Approach 1:
The control system is designed to perform multiple functions: it processes site-specific topographic data, integrates real-time weather condition data, calculates optimal orientations for each row, and controls the orientation mechanisms. This multi-functional approach consolidates what could be separate complex subsystems into a unified control architecture that handles all aspects of adaptive orientation management.
Solution Approach 2:
The system uses readily available data sources (site-specific topographic information and weather condition data) to autonomously determine optimal orientations without requiring complex additional sensing infrastructure. The control system processes this existing data to self-determine the best orientation strategy for each row, reducing the need for elaborate external sensing and control mechanisms.
3Productivity
If each row is oriented independently to optimize total energy output, then productivity is improved, but control system complexity increases due to individual orientation control
Solution Approach 1:
The system implements dynamic orientation control where each row can independently adjust its orientation based on real-time conditions. This dynamic capability allows the system to respond to changing weather conditions and topographic considerations, optimizing energy capture throughout the day and across different rows, rather than relying on static pre-set orientations.
Solution Approach 2:
The control system continuously monitors weather conditions and performance data to adjust the orientation of each row in real-time. This feedback mechanism allows the system to learn from actual performance and environmental conditions, continuously optimizing the orientation strategy for each row to maximize total energy output while adapting to changing conditions.
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
The system efficiently captures both direct and diffuse radiation, optimizing total energy output by coordinating the orientation of each row to maximize global energy production, even in changing weather conditions and complex topographies, resulting in significant energy gains for large-scale solar energy generation.
Implementation Method 1
Solar tracking systems track the trajectory of the sun to more efficiently capture radiation, which is then converted to electrical energy
Data Source
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AI summary
A solar tracking system (200) comprises multiple solar panel modules (SPMi) forming a grid of solar panel modules, wherein the multiple solar panel modules (SPMi) are orientatable to a solar source independently of each other; and a control system (SPCi) configured to orient each of the multiple solar panel modules (SPMi) to the solar source independently of each other based on a performance model to optimize an energy output from the grid of solar panel modules, wherein the performance model predicts an energy output from the grid of solar panel modules based on a topography of the area containing the grid of solar panel modules and weather conditions local to each of the solar panel modules (SPMi).