Solar Cell Test Apparatus Using Learning-Based Output Prediction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The existing methods for manufacturing solar cell modules result in significant output deviations and reduced production efficiency due to the classification of solar cells based solely on efficiency ratings, leading to discrepancies between target and actual output.
Innovation Solution
A test apparatus and photovoltaic system that utilizes an interface to receive cell information, including efficiency, voltage, and current, and a processor to perform learning-based predictions of solar cell module output, incorporating line and module information to reduce output deviations and enhance production efficiency by classifying ratings based on output prediction values.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If solar cells are classified based on cell efficiency rating, then cell quality control is improved, but output deviation of solar cell module increases and production efficiency deteriorates
Solution Approach 1:
The invention changes the classification parameters from single-cell efficiency to a comprehensive model considering cell efficiency, line information, and module configuration. This multi-parameter approach (equation: module output = f(cell efficiency, line information, module information)) resolves the contradiction by providing more accurate output predictions, allowing broader cell selection ranges while maintaining module quality standards, thus improving production efficiency without sacrificing measurement precision.
2Measurement precision
If solar cells are classified based on cell efficiency rating, then cell quality control is improved, but output deviation of solar cell module increases
Solution Approach 1:
The invention implements a feedback mechanism where actual module output data is collected and used to refine the prediction model. The system continuously learns from real-world performance data (line information) to improve the accuracy of output predictions. This feedback loop resolves the contradiction by enabling the system to maintain high measurement precision while progressively improving manufacturing precision through data-driven model optimization.
Solution Approach 2:
The invention creates a composite prediction model that integrates multiple data sources (cell efficiency data, line production information, module configuration data) similar to how composite materials combine different properties. This composite approach (combining cell-level and module-level information) resolves the contradiction by providing a more comprehensive and accurate prediction of module output, thereby improving manufacturing precision while maintaining measurement precision.
3Stability of the object's composition
If strict cell efficiency classification is applied, then module quality consistency is improved, but available solar cells for use decreases
Solution Approach 1:
The invention performs preliminary prediction of module output using the learned model before actual module assembly. This preliminary action (predicting module-level output based on cell characteristics and line information) allows the system to identify suitable cells for each module in advance, expanding the usable cell pool while maintaining output consistency. Cells that would be rejected by strict efficiency classification can be appropriately allocated based on their predicted contribution to final module performance.
Data Source
AI summary
Disclosed are a test apparatus of a solar cell and a photovoltaic system including the same. A test apparatus of a solar cell according to an embodiment of the present disclosure includes an interface to receive cell information including cell efficiency, cell voltage, and cell current and a processor to perform learning based on the cell information of the solar cell and line information of a string line including the solar cell, predict an output of a solar cell module including the solar cell based on the learning, and output an output prediction value of the solar cell module. As a result, an output deviation of the solar cell module including a plurality of solar cells can be reduced.