# 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
\begin{table}
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**Study** & **Approach** & **AI Technique** & **Input Data** & **Fault Type Detected** & **Key Metrics** & **Limitations** \\ \hline [PERSON] and [PERSON] [85] & Fault detection and classification & PNN classification & Irradiance, Temperature, voltage and current of maximum power point & Open circuits, line–line faults & Accuracy: 98.53\% & Requires extensive labeled data \\ \hline [PERSON] et al. [86] & Fault detection and classification & DT classification & Array Voltage, Current, Temperature, Irradiance & Open/Short Circuit, Line–Line & 99.98\% Fault Detection & High training costs, Challenging to handle \\ \hline [PERSON] et al. [87] & Fault detection and classification & RF classification & PV-array voltage and each PV’s maximum power point & Line–Line & 99.994\% Fault Detection & Requires extensive labeled data, computationally intensive \\ \hline [PERSON] and [PERSON] [88] & Fault detection and classification & kNN classification & Array Voltage, Current, Temperature, Irradiance & Open circuit, line-line faults, partial shading & 98.70\% Classification & Limited to experimental conditions \\ \hline [PERSON] et al. [89] & Fault diagnosis & FCM diagnosis & Open-circuit voltage, voltage, Short-circuit, voltage, current, voltage, current and power of maximum power point & 6 faults: Short circuits, & Accuracy: 96\% & Computational efficiency may vary with dataset size \\ \hline [PERSON] et al. [90] & Fault diagnosis & CNN + ETSG and current & PV array voltage and current & Open-circuit faults, line-to-line faults & Accuracy: 99\% & May require significant computational resources \\ \hline [PERSON] et al. [91] & Fault diagnosis & LSTM & PV array voltage, current and power & line-to-line fault (LIF), and hot spot fault (HSF) & 98.78\% and 97.66\% for HSF and LLF & Performance may depend on data quality and variability \\ \hline [PERSON] et al. [92] & Fault detection and diagnosis & ResNet diagnosis & Raw I-V curves, irradiance, and temperature and maximum power & Short circuits, open circuits, degradation, partial shading & Accuracy: 99.94\% & May require extensive training data for optimal performance \\ \hline [PERSON] et al. [93] & Fault diagnosis & Data Fusion + Fuzzy Mathematics & Voltage, Current, Temperature, Irradiance, speed of win & Fault localization in large arrays, robust handling of uncertainties & Enhanced accuracy in complex conditions & Computationally intensive \\ \hline [PERSON] et al. [94] & Fault diagnosis & Neuro-Fuzzy diagnosis & Irradiance, Temperature, I-V curve parameters & Degraded Modules, Noise Issues & Effective in noisy conditions \\ \hline \hline \end{tabular}
\end{table} 
Table 5: _Cont._This analysis explores different strategies for PV system fault detection and diagnosis, focusing on both UAV-based and sensor-based approaches. UAV-based methods excel in capturing high-resolution imagery and are effective for visual fault detection, but they face challenges such as environmental dependency and limited real-time monitoring capabilities. On the other hand, sensor-based methods provide the advantage of continuous monitoring and real-time fault detection. However, these methods also present computational efficiency, data dependency, and scalability challenges. A horizontal comparison of these approaches reveals that while UAV-based methods are advantageous for large-scale solar installations with their ability to cover extensive areas quickly, sensor-based methods may offer better performance in fault detection with a continuous monitoring system. The choice between these approaches depends on specific application scenarios, such as the scale of the PV system, the importance of real-time monitoring, and the type of faults being targeted.