## 3 Machine Learning and Fault Diagnosis
### Simple Fault Diagnosis Models  
#### Artificial Neural Networks (ANN)  
An artificial neural network is a type of supervised learning model that is based on a simplified model of the human brain. These networks consist of highly connected elementary neurons that operate in parallel. Each neuron can have multiple inputs and calculate a single output based on the received information. A hierarchical network structure is always a network, consisting of an input layer, several hidden layers, and an output layer with numerous neurons in each layer, as depicted in Figure 11.  
Artificial neural networks (ANNs) have two key properties that make them useful in the field of production systems diagnostics. Firstly, they are capable of approximating non-linear functions, and secondly, they are well-suited for pattern recognition tasks, such  
Figure 11: Structure of an artificial neural network. Figure 10: Illustration of the structure of Machine Learning in PV fault diagnosis. as PV system diagnosis [79]. ANNs do not rely on mathematical models and are, therefore, applicable to complex systems, making them highly advantageous. However, the learning process, network architecture, and explanation of ANNs have certain limitations [80]. In the literature, ANNs are widely used to characterize PV system failures [81]. For instance, an ANN model has been proposed to identify and detect several faults in a photovoltaic installation, including short-circuit faults, short-circuited bypass diodes, inverted bypass diodes, disconnected bypasses, module open circuits, and connection resistance between modules. The results obtained from the experimental data showed good accuracy in detecting and classifying different defects [79,82]. Additionally, to improve the performance of a PV module, the IV characteristics of a solar cell can be modeled using ANN [83]. A comparison between the electrical equivalent model and the thin film technology model showed good accuracy of the ANN with the crystal technology model. Similarly, a new diagnostic model has been developed that uses current and voltage data at the maximum power point to detect short-circuit and disconnected string faults with the aid of ANN [84]. The comparison between the simulated model values and the actual values showed an accuracy rate of approximately 98.6%. Furthermore, ANNs made it possible to detect the open switch fault of the inverter based on the characteristics of the phase current [85]. This technique was found to provide better control than using basic DTC in terms of sound. A study was carried out using irradiation and temperature data on two PV fields of different capacities (2.2 kWp and 4.16 kWp) to detect partial shading and module chain disconnection faults with the ANN algorithm [10]. The model was found capable of detecting defects with an accuracy that varies between 96.7 and 98.1%, respectively, without and with shading. An accuracy of 97.6% with the 2.2 kWp field and 97% with the 4.16 kWp field was obtained. This decrease is due to the variation in the nature and capacity of the PV field in terms of the amount of data and the number of faults detected. Moreover, the use of ANNs significantly reduced the risks associated with manual repairs and the time necessary for diagnosis when detecting reverse diode faults and partial shading of a photovoltaic module array from simulated data [86]. A technique based on fast Fourier transform and ANN was able to detect open-circuit and short-circuit faults in the 5-level cascaded inverter using the inverter's output voltage [87,88]. [PERSON] et al. showed that ANNs can detect partial shading, short circuits, ground faults, and degradation faults, with an efficiency of approximately 99% for correctly classified defects [89]. An ANN-based approach is proposed to detect and classify series, parallel, short-circuit, and open-circuit resistance faults under different irradiation and temperature conditions in a PV system [90].