Intelligent fault detection of photovoltaic panel using neural networks

Abstract

This research primarily aims to leverage artificial neural network technology for diagnosing power output issues in photovoltaic (PV) panels stemming from fluctuations in solar irradiance and temperature. The proposed diagnostic approach relies on constructing a reference model that captures the expected normal operating behavior of the PV panel under fault-free conditions. This reference model is then compared against the actual power output, and the difference, known as the residual, is analyzed to detect potential faults. The neural network is trained using real-world data inputs like solar irradiance and temperature measurements, with the sole output being the power produced by the PV panel. Through training, the neural network learns to map the complex non-linear relationships between environmental inputs and expected power output, effectively modeling the PV system's intrinsic behavior under healthy conditions. Results demonstrate the neural network-based approach's remarkable ability to diagnose faults with high accuracy while avoiding potential non-linear complications. This intelligent monitoring system provides a reliable protocol for early fault detection through training on actual measurements, eliminating the need for complex mathematical models. Consequently, it streamlines the maintenance process by negating intricate procedures to identify PV panel issues. The neural network effectively learns the mapping between environmental conditions and expected power output through exposure to real-world data during training. By analyzing deviations from this learned mapping, represented by the residual signal, the approach can reliably detect anomalies indicative of faults or performance degradation in the PV system. This data-driven nature allows the system to adapt to site-specific characteristics and capture non-linear effects without explicit modeling.