Prevention of PID Phenomenon for Solar Panel Based on Mathematical Data Analysis Models

You-Shyang Chen, Ying-Hsun Hung (), Yu-Sheng Lin, Jieh-Ren Chang (), Chi-Hsiang Lo and Hong-Kai You
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You-Shyang Chen: College of Management, National Chin-Yi University of Technology, Taichung 411030, Taiwan
Ying-Hsun Hung: Department of Finance, Chaoyang University of Technology, Taichung 413310, Taiwan
Yu-Sheng Lin: Executive Doctoral Business Administration, Dauphine University of Business, CEDEX 16, 75775 Paris, France
Jieh-Ren Chang: Department of Electronic Engineering, National Ilan University, Yilan City 26047, Taiwan
Chi-Hsiang Lo: Department of Electronic Engineering, National Ilan University, Yilan City 26047, Taiwan
Hong-Kai You: Department of Electronic Engineering, National Ilan University, Yilan City 26047, Taiwan

Mathematics, 2023, vol. 11, issue 19, 1-25

Abstract: In recent years, the problem of potential-induced degradation (PID) phenomenon has been deeply associated with solar power issues because it causes serious power attenuation of solar panels and results in lowering its power generation efficiency. Thus, effectively identifying the PID problem from insights of industry data analysis to reduce production costs and increase the performance of power generation is an interesting and important subject for the solar power industry. Moreover, by the traditional standard rule (IEC62804) and the condition of a 96 h testing time, the costs of testing time and assembling materials against PID are very high and must be improved. Given the above reasons, this study proposes a hybrid procedure to organizes four mathematical methods: the mini-module testing, solar cell testing, a settling time, and a neural network, which are named as Method-1–Method-4, respectively, to efficiently solve the PID problem. Consequently, there are four key outcomes from the empirical results for solar power application: (1) In Method-1 with a 96 h testing time, it was found that the large module with higher costs and the mini module with lower costs have a positive correlation; thus, we can replace the large-module testing by the effective mini module for lower cost on module materials. (2) In Method-2 with a 24 h testing time, it was also found that the mini module and the solar cell are positively correlated; this result provides evidence that we can conduct the PID test by the easier solar cell to lower the costs. (3) In Method-3, the settling time achieves an average accuracy of 94% for PID prediction with a 14 h testing time. (4) In Method-4, the experimental result provides an accuracy of 80% when identifying the PID problem with the mathematical neural network model and are obtained within a 2 h testing time. From the above results, these methods succeed in reducing cost of materials and testing time during the manufacturing process; thus, this study has an industrial application value. Concurrently, Method-3 and Method-4 are rarely seen in the limited literature review for identifying PID problem; therefore, this study also offers a novel contribution for technical application innovation.

Keywords: solar cell; potential induced degradation; mathematical models; industry data analysis; neural networks (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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