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The diagnosis of faults in grid-connected photovoltaic (GCPV) systems is a challenging task due to their complex nature and the high similarity between faults. To address this issue, we propose a wrapper approach called the salp swarm algorithm (SSA) for feature selection. The main objective of SSA is to extract only the most important features from the raw data and eliminate unnecessary ones to improve the classification accuracy of supervised machine learning (SML) classifiers. Subsequently, the selected features are used to train supervised machine learning (SML) techniques in distinguishing between various operating modes. To evaluate the efficiency of the technique, we used healthy and faulty data from GCPV systems that have been injected with frequent faults, 20 different types of faults were introduced, including line-to-line, line-to-ground, connectivity faults, and those affecting the operation of bay-pass diodes. These faults present diverse conditions, such as simple and multiple faults in the PV arrays and mixed faults in both arrays. The performances of the developed SSA-SML are compared with those using principal component analysis (PCA) and kernel PCA (KPCA) based SML techniques through different criteria (i.e., accuracy, recall, precision, F1 score, and computation time). The experimental findings demonstrated that the proposed diagnosis paradigm outperformed the other techniques and achieved a high diagnostic accuracy (an average accuracy greater than 99%) while significantly reducing computation time.

Modern photovoltaic (PV) systems have received significant attention regarding fault detection and diagnosis (FDD) for enhancing their operation by boosting their dependability, availability, and necessary safety. As a result, the problem of FDD in grid-connected PV (GCPV) systems is discussed in this work. Tools for feature extraction and selection and fault classification are applied in the developed FDD approach to monitor the GCPV system under various operating conditions. This is addressed such that the genetic algorithm (GA) technique is used for selecting the best features and the artificial neural network (ANN) classifier is applied for fault diagnosis. Only the most important features are selected to be supplied to the ANN classifier. The classification performance is determined via different metrics for various GA-based ANN classifiers using data extracted from the healthy and faulty data of the GCPV system. A thorough analysis of 16 faults applied on the module is performed. In general terms, the faults observed in the system are classified under three categories: simple, multiple, and mixed. The obtained results confirm the feasibility and effectiveness with a low computation time of the proposed approach for fault diagnosis.

The synthesis and solid-state characterization of a new hybrid polyoxometalate of formula (NH 4 ) 2 (C 6 H 18 N 2 )[H 2 P 2 Mo 5 O 23 ]·H 2 O (POM 2) were carried out by using X-ray diffraction, Fourier transform infrared spectroscopy (FT-IR), UV–Vis and fluorescence techniques. It crystallized in a triclinic system with space group P-1, a = 10.1868 (17), b = 10.730(2), c = 14.380(3) Å, α = 100.840(6), β = 95.868(6), γ = 114.934(6)°, and Z = 1. Analysis of the crystal structure reveals that the Strandberg anions [H 2 P 2 Mo 5 O 23 ] 4− are interconnected into a 3D supramolecular inorganic framework through hydrogen bonding interactions involving water molecules, ammonium cations, and the terminal oxygen atoms of polyanionic units. The organic cations are hosted within the anionic framework to balance its negative charge. Density functional theory (DFT) calculations were performed to optimize the geometry of the novel compound and its homologous compound (C 6 H 18 N 2 ) 2 [H 2 P 2 Mo 5 O 23 ]·H 2 O (POM 1), in order to evaluate HOMO–LUMO energy parameters, molecular electrostatic potential (MEP) and non linear optical (NLO) properties. The calculated results reveal good consistency with the experimental structure. The calculated first-order hyperpolarizability of POM 1 and POM 2 are 8.71 and 15.87 times that of urea.

The synthesis and solid-state characterization of a new hybrid polyoxometalate of formula (NH ₄ ) ₂ (C ₆ H ₁₈ N ₂ )[H ₂ P ₂ Mo ₅ O ₂₃ ]·H ₂ O (POM 2) were carried out by using X-ray diffraction, Fourier transform infrared spectroscopy (FT-IR), UV–Vis and fluorescence techniques. It crystallized in a triclinic system with space group P-1, a = 10.1868 (17), b = 10.730(2), c = 14.380(3) Å, α = 100.840(6), β = 95.868(6), γ = 114.934(6)°, and Z = 1. Analysis of the crystal structure reveals that the Strandberg anions [H ₂ P ₂ Mo ₅ O ₂₃ ] ⁴⁻are interconnected into a 3D supramolecular inorganic framework through hydrogen bonding interactions involving water molecules, ammonium cations, and the terminal oxygen atoms of polyanionic units. The organic cations are hosted within the anionic framework to balance its negative charge. Density functional theory (DFT) calculations were performed to optimize the geometry of the novel compound and its homologous compound (C ₆ H ₁₈ N ₂ ) ₂ [H ₂ P ₂ Mo ₅ O ₂₃ ]·H ₂ O (POM 1), in order to evaluate HOMO–LUMO energy parameters, molecular electrostatic potential (MEP) and non linear optical (NLO) properties. The calculated results reveal good consistency with the experimental structure. The calculated first-order hyperpolarizability of POM 1 and POM 2 are 8.71 and 15.87 times that of urea.