# 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
The approach achieved 96% diagnostic accuracy in simulations and experiments, demonstrating scalability to new fault types and adaptability to transient conditions. This method is computationally efficient, independent of labeled datasets, and suitable for integration into SCADA (Supervisory Control and Data Acquisition) systems for real-time monitoring in complex environments. [PERSON] et al. [90] introduced a fault diagnosis method leveraging CNNs and Electrical Time Series Graphs (ETSGs). Their approach converts sequential voltage and current data into 2D ETSGs, allowing the CNN architecture to automatically extract features. The model achieved over 99% accuracy in diagnosing open-circuit and line-line faults in experimental case studies. To address the challenges of manual feature extraction, [PERSON] et al. [91] presented a novel fault diagnosis technique using LSTM networks to automatically extract features from raw data for fault classification. The LSTM model outperformed traditional methods such as SVMs, ANNs, and PNNs, achieving high diagnostic accuracy even with noisy data, thereby eliminating the need for manual feature extraction. Similarly, [PERSON] et al. [92] proposed a deep residual network (ResNet)-based fault detection and diagnosis model that addresses the limitations of manual feature extraction. By utilizing raw I-V curves, irradiance, and temperature data, the ResNet model demonstrated superior accuracy, reliability, and training efficiency compared to CNNs and other techniques, proving effective across simulated and real-world datasets. Several studies focused on addressing uncertainties in PV systems. [PERSON] et al. [93] introduced a data fusion approach using fuzzy mathematics and evidence theory, which consolidates differences between measured and predicted values to improve fault localization in large-scale PV systems. [PERSON] et al. [94] employed a Neuro-Fuzzy approach for PV module parameter estimation under different conditions. Their AI-based diagnostic system accurately differentiates between normal and faulty operations, even in noisy environments. Additionally, [PERSON] et al. [95] evaluated unsupervised anomaly detection models for identifying rare faults in PV systems. Their study compared multiple models, with VAE achieving the highest detection rate of 92.06%, demonstrating the potential of unsupervised learning for real-time monitoring in environments with limited labeled data. While much attention has been given to fault detection in PV modules and arrays, recent works have also explored fault diagnosis in the associated power conversion systems, which are crucial to ensure stable energy transfer and grid integration. In this regard, [PERSON] et al. [96] proposed a novel fault diagnosis method combining feature transformation and Random Forests to identify open-circuit faults in IGBT switches within a three-phase PWM rectifier. Their approach leverages current trajectory slopes as features to enhance classification performance across varying load conditions, achieving high accuracy in localizing switch faults. [PERSON] et al. [97] developed an LSTM-based fault detection method for identifying High Impedance Faults (HIFs) using Wavelet-Transformed current features to enhance diagnostic accuracy in solar-integrated grids. Validated on a PV-integrated IEEE 13-bus system, the model achieved over 91% accuracy, outperforming other classifiers and confirming the efficacy of deep learning in grid fault classification. In addition, [PERSON] et al. [98] introduced a hybrid-driven approach based on model data to detect open switch faults in converters. By fusing model-based analysis with ANN learning, their method offers near-instantaneous fault diagnosis under dynamic conditions, achieving high robustness and low computational burden. Although sensor-based approaches offer significant advantages, challenges such as computational efficiency, data requirements, and scalability remain areas for future research.