## 1 Introduction  
With the significant increase in the necessity of photovoltaic (PV) energy generation to curb climate change, the installation of large PV plants has grown significantly in the last decade [1]. As it is desirable to operate these plants at their maximum capacity, monitoring the performance of the installed PV modules is critical [2]. Cracks in solar cells have received significant attention in the last years [3]. Cracks are often classified into three modes: micro-cracks (Mode A) and cracks (Mode B and C) [4]. Generally, a micro-crack Mode A does not have a significant impact on the output power. The loss due to the impacted cell area is relatively low, as long as the different regions are electrically connected [4]. However, cracks (Mode B and C) do affect the power output of the PV module. Cells with Mode B cracks exhibit an increase in resistance and lower voltage in the cracked regions [5], while cells with Mode C form a wholly isolated and electrically disconnected cell area. In some cases, cracks (Mode B and C) lead to reverse biasing of the solar cell [6]. In others, 16-25% of the cell area can be separated by cracks parallel to the busbars [7]. The cracks can form due to mechanical stress during transportation or manufacturing installation and maintenance of the modules [7]. A brief classification of the different crack modes is provided in Reference [8]. Another common extrinsic fault type is finger interruptions, usually induced during cell metallization and module interconnection. Finger breaks often result in increased series resistance and, consequently, decreased output power [9; 10]. Electroluminescence (EL) imaging has become an indispensable tool for distinguishing various types of failures and different degradation mechanisms with high resolution [11]. EL imaging is based on biasing the modules and measuring the emitted emission, which correlates with the radiative recombination of carriers within the device [12]. As the local luminescence intensity is related to the carrier concentration, faulty and disconnected regions appear darker, depending on the severity of the fault. EL imaging has been used to detect a wide range of defects, such as micro-cracks and cracks, finger interruptions, ribbon damage, and many more [3]. EL imaging can also quantify power losses and the percentage of disconnected regions due to the gap between cell parts and cracks by modifying the bias current [6; 13]. Analyzing EL images is typically time-consuming [14] and requires expert knowledge regarding the different defects. It is, therefore, expensive to perform on a large scale [15]. One possible path to improve the analysis is using machine learning (ML) to detect different defects more accurately. A recent advancement has meant outdoor PL images could be obtained by switching the operating module condition through modulating the shading on three cells connected to three different bypass diodes using a high-power light-emitting diode (LED) array. PL has an additional benefit over EL, as it is a contactless technique, and therefore does not require a qualified electrician to change any wiring of the inspected PV system [16]. ML uses predictive or descriptive algorithms to optimize a performance model using a given dataset [17]. The models are built based on the available data to make predictions without being explicitly programmed to perform the task [18]. This study investigates the use of machine learning (ML) to classify the defects mentioned above. Recently, different ML algorithms have been used to classify various degradation types in EL images. [PERSON] et al. used a supervised ML algorithm to classify a database of 14,200 images into three labels: good, busbar corrosion, and cracked [15]. They used three ML algorithms [support vector machine (SVM), random forest (RF), and multilayer perceptron-artificial neural network (MLP-ANN)] and compared their performance.