Multi‐attribute analysis of micro‐defect detection techniques suitable for automated production line of solar wafers and cells

Abstract

Renewable energy is going to play an immensely important role in the coming decades. Hence, the bulk manufacturing of solar panels has seen a renewed interest. For this purpose, it is imperatively important to assess the techniques that can be employed on automated production lines for evaluating the quality of solar wafers and cells. To-date many non-destructive testing methods to access the micro-defects in solar wafers and cells have been developed around the globe; however, not all of them can be applied on a fast-paced production line. In this regard, this study details all the nondestructive evaluating techniques that can be employed on an automated production line. Using a multi-attribute decisionmaking method, a strategic evaluation procedure is developed that can be adopted for the optimum selection of evaluation tools currently available in the market. This study is aimed at young researchers, graduate students and practitioners who want to come up-to-speed regarding the various defects that occur in solar wafers and cells along with the techniques that are currently being adopted to evaluate those defects. Also, future trends in research are highlighted regarding the development of assessment techniques.

Imaging methods of detecting defects in photovoltaic solar cells and modules: a survey

“…Under the influence of incident light, the photocurrent is generated locally and then a bitmap is created [40]. Tests using it are best carried out at room temperature [48], isolating the system from external sources of radiation [54,55].…”

“…The wavelengths of excitation light are in the range of 400-1200 nm [29], [36,40,48,[54][55][56][57][58][59]. It allows the examination of monocrystalline and polycrystalline cells, perovskites, or silicon heterojunctions [55], [56,58,59]. To ensure the highest possible precision of the created bitmaps, a step of 0.1-25 μm is used [29,40,59].…”

Surface Defect Recognition of Solar Panel Based on Percolation-Based Image Processing and Serre Standard Model

“…The optimal selection of evaluation tools currently available in the market was also made. The purpose of the study is to understand the defects of different types of solar wafers and solar cells, and to understand existing detection methods [6]. A deep learning algorithm was proposed without relying on the electroluminescent imaging device, chip module design, and imaging quality.…”

Fundamental study related to the development of modular solar panel for improved durability and repairability

“…In this regards, solar energy offers an attractive solution to the leaders that allows them to connect their population to the national grid using fast and cheap alternatives [1,2]. Similar considerations are valid for war-torn countries such as Libya, Syria, and Yemen where traditional solutions require a huge investment and long-time to construct them, while renewable energy solutions appear as a quick and reliable solution that can be implemented at grass root level [3]. One of the key challenges faced by the solar industry is the durability and repairability of the solar panels (PV).…”

“…The multiplication of these values gives the actual maximum power (P max ). One more parameter that links the theoretical and actual produced power is the fill factor, FF, calculated using Equation (3). The fill factor represents the power fraction that can be acquired from a module under STC, in other words it is the measure of a module quality in collecting power from the maximum available power generated by the cells [18].…”