# Artificial Intelligence of Things for Solar Energy Monitoring and Control
## 3 AIoT Applications in PV Systems: A Comprehensive Review
### Applications of AI in PV Systems
#### 3.1.1 Fault Detection and Diagnosis  
Fault detection and diagnosis are critical for maintaining the performance and reliability of PV systems. Timely detection and accurate classification of faults, such as short circuits, open circuits, and shading, are essential to minimize energy losses, prevent equipment damage, and reduce maintenance costs. Traditional fault detection methods often rely on manual inspections or simple threshold-based techniques, which may be insufficient for handling the complexity and variability of modern PV systems. Recent advancements in Artificial Intelligence have enabled the development of more sophisticated and efficient approaches for detecting and diagnosing a wide range of faults, even under dynamic and uncertain operating conditions. Several studies have demonstrated the effectiveness of combining UAV imagery with AI algorithms for fault identification. For instance, [PERSON] et al. [69] developed an ensemble-based Deep Neural Network (DNN) model designed for the autonomous detection of visual faults in PV modules, such as glass breakage, burn marks, discoloration, and delamination. Utilizing an image dataset captured by an RGB camera mounted on an unmanned aerial vehicle (UAV), the process begins with image preprocessing, which involves extracting spatial and frequency domain features using methods like discrete Wavelet Transform (DWT), texture analysis, grey level co-occurrence matrix (GLCM), fast Fourier transform (FFT), and grey level difference method (GLDM). The processed images are then input into the DNN model for fault detection. Similarly, [PERSON] et al. [70] applied a deep learning model with Region-Based Histogram Approach (RHA) preprocessing on RGB images, followed by the Grey Scale Quantization algorithm (GSQA) to refine image details, detecting panel defects such as spots, cracks, dust, and microcracks. The model was tested with different dataset sizes (500, 1000, and 2000 images), highlighting improved performance with larger datasets. Also, [PERSON] et al. [71] employed a Support Vector Machine (SVM) with Histogram Equalization (HE) preprocessing to detect microcracks in PV cells based on the ELPV dataset. While highly accurate, the reliance on RGB images alone might miss thermal anomalies, such as hot spots, which are better captured by IR imaging. [PERSON] et al. [72] developed PV-YOLO, a modified YOLOX model, to detect and classify multiple faults in PV panels using drone-captured infrared (IR) images. This method demonstrates high accuracy in detecting multiple faults but may struggle under diverse environmental conditions that affect IR image quality. [PERSON] et al. [73] modified YOLOv5 to detect defects that pose electrical hazards in PV panels. While [PERSON] et al. [74] proposed a YOLOv5s model enhanced with Gaussian and Wavelet Transform preprocessing to detect panel conditions. In [75], researchers proposed a solution for automatically detecting and classifying the condition of PV panels using deep learning and UAV technology. The method combines YOLO-based deep learning models (YOLOv5, YOLOv7, and YOLOv8) with drone-captured images to classify panels as normal, dusty, or damaged. The system involves a dataset of 1100 RGB images processed with Histogram Equalization to enhance image quality before being fed into the model. Real-time testing with a Raspberry Pi 4B on a UAV demonstrated the method's effectiveness in accurately detecting and categorizing faults in solar panels. UAV-based methods provide high-resolution imaging and scalability, making them invaluable for large-scale solar installations. However, they face challenges such as dependency on environmental conditions, infrastructure requirements, and the need for robust preprocessing to enhance image quality. Additionally, their reliance on periodic inspections rather than continuous monitoring limits their effectiveness for real-time fault detection. A detailed comparison of the UAV-based approaches is provided in Table 4, summarizing their methodologies, metrics, and limitations.