# Fault Diagnosis in Solar Array I-V Curves Using Characteristic Simulation and Multi-Input Models
## 1 Introduction  
In recent years, with the development of the green energy industry and the implementation of sustainable energy policies by various countries, the construction of solar power plants has been expanding continuously, and the prospects of the solar energy market have been growing. However, due to prolonged operation in extreme and harsh natural environments, various faults are inevitable, leading to a significant reduction in actual service life. When a module fails, the direct risks include damage to the module itself, which in turn reduces the power generation efficiency; the indirect risks may cause the entire solar photovoltaic (PV) power generation system to malfunction, adversely affecting the grid and resulting in severe economic losses. Image-based solar fault diagnostic methods use image processing and analyze different images, such as visible light, infrared thermography [1], and electroluminescence images [2; 3], to detect partial shading, hot spot faults, and crack faults in solar modules. These methods require the use of additional sensors to collect data and, thus, are time-consuming and costly. To address the maintenance issues brought about by the rapidly increasing demand for large-scale solar panel installations in the future, this study aims to utilize deep learning in the string characteristics of solar systems. It establishes a fault diagnostic system for solar energy systems, diagnosing the I-V curves of solar string and utilizing deep learning algorithms to develop the required models to reduce manpower resource costs, thus more efficiently maintaining the solar arrays and increasing the power generation efficiency and stability, reducing the potential hazards caused by faults. The current-voltage (I-V) curve of a solar string is a graphical representation of the relationship between the current and voltage outputs under fixed irradiance and temperature conditions. From the curve, some main parameters can be found, includingopen-circuit voltage (\(V_{oc}\)), short-circuit current (\(I_{sc}\)), maximum power point voltage (\(V_{mpp}\)), maximum power point current (\(I_{mpp}\)), maximum power (\(P_{max}\)), and fill factor (FF) [4; 5; 6]. Most current fault diagnostic methods based on the I-V curve rely on feature extraction, summarizing the curve as a set of parameters and then using statistical methods [7] or support vector machines [8] to evaluate the parameters for fault classification, or using the entire I-V curve; employing automatic feature extraction with principal component analysis [9] or analysis through a two-dimensional convolutional neural network (2D-CNN) [10], and long short-term memory (LSTM) networks [11]. Unlike previous methods, this study differs by using a multiple-input model that simultaneously inputs the entire I-V curve and the feature values extracted from the curve into the model, allowing the model to obtain more information from the curve to aid in fault classification. Most literature indicates that it is not easy to obtain a large quantity of current-voltage curve data for faults, and this study addresses this issue through simulation. Considering the difficulty of collecting a large quantity of fault current-voltage curves from actual field sites as training data for the model, this study simulates a large number of fault characteristic curves of solar system string using MATLAB Simulink version 10.7 (R2023a) as training data for the fault diagnostic model, including voltage-current curves, irradiance, specifications of solar module short-circuit current, specifications of solar module open-circuit voltage, and the number of modules in a solar string, etc. The simulated data are preprocessed before being inputted into the multiple-input model for training.