Cracks in solar cells, caused in production or assembly, can considerably affect the degradation of a cell in the field [1]. Predicting the impact of these cracks improves quality control and helps to cope with degradation throughout the lifetime of a solar cell.
Although information about photovoltaic module degradation has been available since the early 1970s, predictions for different types of degradation are still poorly studied [2].
This thesis aims at developing a new approach to predict the aging of solar cells using Deep Learning, given an initial electroluminescense (EL) measurement of the latter. The data used in this thesis consists of 2 measurements of 94 modules at different points in time. We will
- use this dataset to train an unpaired Image2Image approach (e.g. CycleGAN [3]) to assess, if the network is capable of learning the relationship between initial and aged measurements from data, using unpaired datasets only
- extend the approach in I. to incorporate the additional information available from using pairs of initial and aged measurements.
Since the initial and aged measurements are not registered exactly, we aim to design a custom loss function in 2. that is invariant to small registration mismatches, but enforces consistency between cracks in generated and real aged measurements. We want to assess, if the weakly supervised crack segmentation by Mayr et al. [5] can be used for that purpose. To this end, we plan to enforce consistency between the coarse segmentation maps of real and aged measurements. This can be seen as an extension to the common combination of adversarial loss with L1/L2 distance between fake and real target image [3].
The purpose of the L1/L2 distance in CycleGAN can be seen as enforcing consistency between input and output of the generative network. Since our custom loss compares the generated fake cell to the real aged cell, the input/output consistency for the generative network can possibly be ensured without taking into account the L1/L2 distance between generator input and output. Apart from combining the two common losses with our custom loss, we therefore want to evaluate whether we can get better results by replacing the L1/L2 distance altogether.
A prototype of this network will be realized in Pytorch, based on implementations of [3] and [4].
References:
[1] Quintana, M.A., King, D.L., McMahon, T.J., Osterwald, C.R., 2002. Commonly observed degradation in field-aged photovoltaic modules. In: Proc. 29th IEEE Photovoltaic Specialists Conference, pp. 1436–1439.
[2] Ndiaye, A., Charki, A., Kobi, A., Kébé, C.M., Ndiaye, P.A. and Sambou, V., 2013. Degradations of silicon photovoltaic modules: A literature review. Solar Energy, 96, pp.140-151.
[3] Zhu, Jun-Yan, Taesung Park, Phillip Isola, and Alexei A. Efros. “Unpaired image-to-image translation using cycle-consistent adversarial networks.” In Proceedings of the IEEE international conference on computer vision, pp. 2223-2232. 2017.
[4] Mayr, Martin, Mathis Hoffmann, Andreas Maier, and Vincent Christlein. “Weakly Supervised Segmentation of Cracks on Solar Cells Using Normalized L p Norm.” In 2019 IEEE International Conference on Image Processing (ICIP), pp. 1885-1889. IEEE, 2019.