Fault Detection In Photovoltaic Arrays Research Articles

A Novel Hybrid Optimization Approach for Fault Detection in Photovoltaic Arrays and Inverters Using AI and Statistical Learning Techniques: A Focus on Sustainable Environment

Fault detection in PV arrays and inverters is critical for ensuring maximum efficiency and performance. Artificial intelligence (AI) learning can be used to quickly identify issues, resulting in a sustainable environment with reduced downtime and maintenance costs. As the use of solar energy systems continues to grow, the need for reliable and efficient fault detection and diagnosis techniques becomes more critical. This paper presents a novel approach for fault detection in photovoltaic (PV) arrays and inverters, combining AI techniques. It integrates Elman neural network (ENN), boosted tree algorithms (BTA), multi-layer perceptron (MLP), and Gaussian processes regression (GPR) for enhanced accuracy and reliability in fault diagnosis. It leverages its strengths for the accuracy and reliability of fault diagnosis. Feature engineering-based sensitivity analysis was utilized for feature extraction. The fault detection and diagnosis were assessed using several statistical criteria including PBAIS, MAE, NSE, RMSE, and MAPE. Two intelligent learning scenarios are carried out. The first scenario is conducted for PV array fault detection with DC power (DCP) as output. The second scenario is conducted for inverter fault detection with AC power (ACP) as the output. The proposed technique is capable of detecting faults in PV arrays and inverters, providing a reliable solution for enhancing the performance and reliability of solar energy systems. A real-world solar energy dataset is used to evaluate the proposed technique with results compared to existing detection techniques and obtained results showing that it outperforms existing fault detection techniques, achieving higher accuracy and better performance. The GPR-M4 optimization justified its reliably among all the models with MAPE = 0.0393 and MAE = 0.002 for inverter fault detection, and MAPE = 0.091 and MAE = 0.000 for PV array fault detection.

Simulation of fault detection in photovoltaic arrays

In solar systems, faults in the module and inverter occur in proportion to increased operating time. The identification of fault types and their effects is important information not only for manufacturers but also for investors, solar operators and researchers. Monitoring and diagnosing the condition of photovoltaic (PV) systems is becoming essential to maximize electric power generation, increase the reliability and lifetime of PV power plants. Any faults in the PV modules cause negative economic and safety impacts, reducing the performance of the system and making unwanted electric connections that can be dangerous for the user. In this paper have been classified all possible faults that happen in the PV system, and is presented to detect common PV array faults, such as open-circuit fault, line-to-line fault, ground fault, shading condition, degradation fault and bypass diode fault. In this studies examines the equivalent circuits of PV arrays with different topological configurations and fault conditions to evaluate the effects of these faults on the performance of a solar system, taking into account the influence of temperature and solar radiation. This work presents the validation of a simulated solar network by measuring the output curves of a low-power photovoltaic array system under real outdoor conditions. This method can be useful in future solar systems.

Methodology for automatic fault detection in photovoltaic arrays from artificial neural networks

This article presents a methodology for automatic fault detection in photovoltaic arrays. Due to the great importance in the construction of increasingly robust photovoltaic plants, automatic fault detection has become a necessary tool to extend the useful life of these plants, avoid system shutdowns and reduce serious safety problems. In the present study, nine possible faults are detected, caused by malfunction in the bypass and blocking diodes. The solution consists of training two models based on artificial neural networks, the first model is a binary classifier that detects whether or not a fault occurs, the second is a multiclass classifier that detects the fault type. The obtained models were trained from simulation data, in an architecture of 9 photovoltaic panels interconnected in three rows by three columns matrix (extendable to larger systems). The evaluation shows that the prediction system has a total accuracy of 92.95%. Finally, this methodology is intended to be implemented in Colombia, in zones with difficult access and not interconnected to the electricity grid, seeking to reduce corrective maintenance.