## 4 Measures to Overcome the Challenges
### Faults Occurrence in the BIPV System  
The different types of faults that arise in the module are the optical degradation in a PV module, electrical mismatches and degradation, and non-classified faults. The optical degradation occurs mainly due to the formation of bubbles, and discoloration of the encapsulation. This can occur internally as well externally, internally poor quality of encapsulation and externally high outdoor temperature and humidity are significant factors [8]. In electrical degradation and mismatch, the cracks occur in cells, the interconnection is broken, short-circuiting of cells and shading on the cell are some of the common factors that can arise. In non-classical faults, the bypass diode becomes defected or short-circuited or the submodule becomes open-circuited [8]. The diagnosis of such faults can be performed through infrared thermography conducted to access the condition of the PV modules in a free standing or building attached manner. From the combination of the different IR images, various types of faults and their effect on the I-V curve can be seen. The gathered  
Figure 11: Proposed architecture for automatic fan cooling using IoT and LoRa. IRT can predict the geometry and area of hot spots formed on a single or multiple cells of PV modules, known as a region of interest (Figure 12). The manual capturing of IR images (Figure 13) of a small-scale PV plant of a few kW is possible, however, at a large scale the PV system on building rooftops or integrated on the facades of high-rise buildings requires more reliable and faster technologies [54]. Recently, aerial technologies have evolved to capture the IR images of large PV plants installed on open fields. Using the thermal orthophotoplan, a uniform scale of the captured images is converted into IR maps for predicting different faults that occur in a system. The thermal signatures of PV modules in the IRT mapping were implemented with the help of aero triangulation, photogrammetry, and global positioning system (GPS) data processing (Figure 14). All types of faults detected from the mapping were successfully validated and diagnosed [54]. A digital twin technology can also be developed by these drone aircraft which includes thermal imagery, of both potential PV sites and installed facilities for operational management and troubleshooting (Figure 15) [56]. The operation and maintenance team can scan the serial numbers of the module and can perform an inspection of the PV modules. Figure 12: Schematic diagram of infrared images and possible defects [8]. Figure 13: A manually captured thermographic image of small-scale PV modules [55]. Real-time fault detection in BIPV systems is urgently required to enhance the infrastructure with intelligent technologies for minimizing the loss of PV panels for late detection and diagnosis. In previous studies, a sensing-based system is implemented to detect a fault on the ground-level PV panels. In the case of a building, the area is very large, and it is difficult to identify the different types of faults with the sensing-based system. In this study, an architecture is proposed with the integration of drone technology, edge computing, and AI to realize real-time fault detection. Figure 16 illustrates the working of this real-time fault detection system, where the drones capture the BIPV panel images with the assistance of a thermography camera. The thermography images are immediately transmitted to the edge device which is placed on the ground level of the building through LoRa communication. Figure 14: Implementation of a thermal orthophotoplan technique, for IRT mapping and fault detection in a PV plant [56]. Figure 15: A Raptor Maps drone gathers inspection data on a Borrego solar facility [57]. The edge device is based on edge computing, where it performs the computation process with the help of a co-processor by applying AI/ML models for identifying the faults from the thermography images.