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.

1. Background: Urea is the main product of the nitrogenous breakdown of protein metabolism in mammals. In this study, process intensification for enzymatic hydrolysis of urea by urease enzyme (jack bean urease) was examined in a membrane reactor. 2. Methods: Batch and continuous enzymatic hydrolysis reactions were performed at different substrate concentrations to determine the digestibility and affinity of the substrate with that of the enzyme. The hydrolysate samples were obtained by an optimized continuous enzyme membrane reactor (EMR) coupled with an ultra-filtration membrane (250 kDa). Feed concentration varied from 100 to 500 mg/L. Laboratory experiments were conducted at room temperature (20 ± 1 °C), with a flow rate of 20 mL/min, urease concentration of 0.067 g/L, ionic strength (I = 0, 0.01, 0.05), and ammonium nitrogen addition of (0, 100 mg/L, 200 mg/L, 500 mg/L). Moreover, the effect of ionic strength, ammonium nitrogen concentration, feed concentration, and enzyme concentration on urea hydrolysis was examined. Scanning electron microscopy (SEM) and energy dispersive X-ray spectroscopy (EDAX) analysis were used to identify the physicochemical properties as well as the elemental composition of the Ultra-Filtration membrane used in this study. 3. Results: The study revealed that higher ionic strength and higher concentrations of NH₄SO₂ and ammonium nitrogen (NH₃-N) inhibithydrolysis of urea by reducing the urease enzyme activity in the system over time. 4. Conclusions: Herein, a sustainable alternative for the conversion of urea to ammonia by utilizing urease in an EMR was demonstrated.

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 their strengths for 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 done for PV array fault detection with DC power (DCP) as output. The second scenario is done 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. Real-world solar energy dataset is used to evaluate the proposed technique, with results compared to existing detection techniques and obtained results show that it outperforms existing fault detection techniques, achieving higher accuracy and better performance. The GPR-M4 optimization justified 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.