Methods and systems for detecting shading for solar trackers
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Solution Overview
Problem
Conventional solar tracking systems are inadequate in maximizing energy production from solar panels due to suboptimal sun angles and shading issues, which limit the full potential of energy conversion.
Innovation Solution
A solar tracker system with a tracker controller that includes a processor, memory, power supply, current sensing unit, DC-DC power converter, and motor controller, utilizing machine learning algorithms to predict shading based on geographical and temporal data, and adjust PV tilt angles for maximum output power, addressing both south-north and east-west shading.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional solar tracking mechanisms are used to follow the sun, then solar panels can be positioned for maximum energy production, but energy conversion is still limited due to suboptimal sun angles and shading issues
Solution Approach 1:
The system performs preliminary actions by predicting shading conditions before they occur and pre-adjusting the PV panel orientation accordingly. The machine learning algorithm forecasts shading based on geographical and temporal data, allowing the tracker to proactively optimize its position rather than reactively responding to shading when it occurs.
Solution Approach 2:
The system dynamically adjusts the PV panel orientation based on real-time shading predictions and actual power output measurements. The tilt angle and azimuth are continuously modified to adapt to changing shading conditions throughout the day and year, maximizing energy capture despite dynamic environmental obstacles.
2Power
If solar panels are positioned to maximize energy production, then power output increases, but shading from geographical features and obstacles limits the full potential of energy conversion
Solution Approach 1:
The system implements feedback by continuously measuring the actual power output from PV strings and comparing it against expected values. When shading is detected through power output anomalies, the machine learning algorithm adjusts the panel orientation to compensate, creating a closed-loop control system that actively counteracts shading effects.
Solution Approach 2:
The system replaces traditional mechanical shading avoidance mechanisms with an intelligent control system that uses machine learning algorithms and sensor data to predict and respond to shading conditions. Instead of physical structures to block shade, the system uses computational prediction and dynamic repositioning.
3Loss of time
If a single solar module is used to power the tracker motor, then the system achieves self-powered operation with minimal load, but the module cannot generate sufficient power when shaded or at suboptimal angles
Solution Approach 1:
The system uses multiple PV strings (excessive action) to ensure sufficient power availability for motor operation, even when some strings are shaded or suboptimal. This redundancy ensures that the tracker can always find enough power to operate, sacrificing some potential energy generation capacity to guarantee motor drive reliability.
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 effectively tracks the sun's position to optimize energy production by predicting and mitigating shading, ensuring maximum power output from solar panels, even in partially shaded conditions.
Implementation Method 1
The solar modules include a plurality of PV strings
Data Source
AI summary
A solar tracker system including a tracker apparatus including a plurality of solar modules, each of the solar modules being spatially configured to face in a normal manner in an on sun position in an incident direction of electromagnetic radiation derived from the sun, wherein the solar modules include a plurality of PV strings, and a tracker controller. The tracker controller includes a processor, a memory, a power supply configured to provide power to the tracker controller, a plurality of power inputs configured to receive a plurality of currents from the plurality of PV strings, a current sensing unit configured to individually monitor the plurality of currents, a DC-DC power converter configured to receive the plurality of power inputs powered from the plurality of PV strings to supply power to the power supply, and a motor controller, wherein the tracker controller is configured to track the sun position.