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1. Introduction
As a result of energy crisis and environmental deterioration, solar energy has received more and more attention as it is a clean and renewable energy (Dolara et al., 2016; Vaněk et al., 2016). The installed capacity of global photovoltaic (PV) will exceed 756 GW by 2050 (Global, 2016). With its better energy-efficiency ratio compared with monocrystalline solar cell, the multicrystalline solar cell is widely applied in PV system. The multicrystalline solar cells are key device of PV system; therefore, the production quality of them is very important. However, some micro-cracks maybe generate in multicrystalline solar cell surface during the manufacturing process because of uneven pressure or improper operations.
The durability of PV modules will be affected severely by the micro-cracks of solar cell surfaces due to the resulting electrical power-loss (Paggi and Sapora, 2013). Specifically, the micro-cracks can lead to large electrically disconnected areas or “inactive” areas in solar cells, for individually maximum power tracked modules, the power loss is roughly proportional to the inactive cell area when the inactive area increases beyond 8 per cent of the cell area. Moreover, the greater inactive cell area also results in increased risk of hotspots and other safety issues (Gade et al., 2015). Therefore, the micro-cracks detection of multicrystalline solar cell surface is essential to improve the durability of PV modules and product qualification rate.
Many existing micro-cracks detection methods focused on the analysis of electroluminescence (EL) images. Gade et al. (2015) proposed that the evolution of crack types and power loss due to cell cracks can be analyzed by periodic characterization using EL images. An EL image analysis technique is presented by Infuso et al. (2014) to identify grains and grain boundaries in solar cell surface, and the nonlinear finite element analysis is used to show how grain boundaries and silicon bulk properties influence the crack pattern. An experimental study based on the EL technique is presented by Paggi et al. (2014) to analysis the crack propagation in solar cells embedded in PV modules.
Beyond that, the automated micro-cracks detection methods based on machine vision have been widely used in industry because of its convenience and efficiency (Sun et al., 2016; Teo and Abdullah, 2016; Tsai et al., 2015)....