Partial shading (PS) is a prevalent phenomenon that often affects photovoltaic (PV) installations, leads to the appearance of numerous peaks in the power-voltage characteristics of PV cells, caused by the uneven distribution of solar irradiance on the PV module surface, known as global and local maximum power point (GMPP and LMPP). In this paper, a new technique for achieving GMPP based on the dandelion optimizer (DO) algorithm is proposed, inspired by the movement of dandelion seeds in the wind. The proposed technique aimed to enhance the efficiency of power generation in PV systems, particularly under PS conditions. However, the DO-based MPPT is compared with other advanced maximum power point tracker (MPPT) algorithms, such as Particle Swarm Optimization (PSO), Grey Wolf Optimization (GWO), Artificial Bee Colony (ABC), Cuckoo Search Algorithm (CSA), and Bat Algorithm (BA). Simulation results establish the superiority and effectiveness of the used MPPT in terms of tracking efficiency, speed, robustness, and simplicity of implementation. Additionally, these results reveal that the DO algorithm exhibits higher performance, with a root mean square error (RMSE) of 1.09 watts, a convergence time of 2.3 milliseconds, and mean absolute error (MAE) of 0.13 watts.

This research introduces a novel artificial intelligence (AI) framework for fault detection and diagnosis (FDD) in photovoltaic (PV) systems that combines Convolutional Neural Networks (CNNs) with time–frequency analysis via the Wigner–Ville Distribution (WVD). The proposed method transforms raw numerical measurements—including solar irradiance, temperature, voltage, current, and power—into compact 6 × 12 time–frequency image representations, enabling effective spatial feature extraction by CNNs that are well suited to image-like data. The framework is benchmarked under both noiseless and noisy conditions on a comprehensive 17‑class dataset comprising one healthy condition (C0) and sixteen fault types (F1–F16), including progressive short‑circuit faults within a single string, pure partial‑shading faults, and combined inter‑string short‑circuit and asymmetric partial‑shading patterns along PV strings. To contextualize performance, the CNN–WVD model is compared not only with classical Artificial Neural Networks (ANNs) and Deep Neural Networks (DNNs) but also with Gradient Boosting Machines (GBM), Random Forests (RF), Support Vector Machines (SVM), and k‑Nearest Neighbors (kNN), all trained on the same WVD‑transformed data. In noiseless conditions, ANN and DNN achieve 99.51% and 99.49% accuracy, respectively, while the CNN attains 97.09%; RF, SVM, GBM, and kNN reach 93.47%, 88.62%, 84.01%, and 75.69% accuracy. Under noisy conditions that emulate real PV environments, the CNN is the most robust model with 90.27% accuracy, outperforming ANN (82.20%), RF (82.80%), SVM (83.85%), GBM (73.85%), DNN (76.27%), and kNN (72.80%). Key contributions include: (i) the use of WVD to obtain highly informative time–frequency representations of PV electrical signals, (ii) a structured data‑organization strategy that maps multivariate PV measurements into fixed‑size WVD images, and (iii) a CNN architecture that preserves high discrimination capability across closely related fault severities and locations, even in the presence of noise achieving 90.8% accuracy under realistic sensor noise (\(1 \times\) baseline uncertainty: \(\pm 10{W \mathord{\left/ {\vphantom {W {m^{2} }}} \right. \kern-0pt} {m^{2} }}\) irradiance, \(\pm 2 \, C\) temperature, \(\pm 5 \, V\) voltage, \(\pm 1 \, A\) current, \(\pm 25 \, W\) power) and maintaining 71.5% accuracy at \(3 \times\) noise, representing extreme aging sensor conditions. With a competitive degradation of only 8.91 percentage points—lower than the neural-network baselines (ANN: 16.27%, DNN: 15.00%) and the tree ensemble RF (11.34%)—the CNN + WVD framework demonstrates superior noise robustness for long-term deployment in real-world PV installations. By bridging advanced time–frequency analysis with deep learning and systematically comparing against a broad set of machine‑learning baselines, the proposed framework enables fully automated, fine‑grained PV fault classification without manual feature engineering, thereby enhancing monitoring reliability, reducing downtime, and supporting predictive maintenance in large‑scale PV deployments.

This proposed paper is going to deal with the main new methods which are applied in both of the simultaneous dynamic eccentricity and broken rotor bars diagnosis inside the induction machines. The proposed method is based on the Fast Fourier transform, or spectral analysis of the measured magnetic flux density in the air-gap. More particularly, this paper deals with the use of a finite element method capable of modeling the induction machines which is called Finite Element Model of an electromagnetic calculation from the Maxwell's equation. A various techniques in the literatures are used, such as motor current signature analysis during operation. This work also proposes the internal magnetic flux density signature. Various models are extended to define the method of creating faults around the machine, whereas each model is specified by its technique. The main technique in this work is based on a finite element method, where the faults of rotor bars are created by deleting's operation of the boundary and the field of the rotor bar, which means it will be a part of the air-gap, after that, the dynamic eccentricity is formed by the movements of the rotating center of the rotor with different ratings. The results of these simulations are presented and could be considered as a rotor's failed diagnostics in the induction machine. Before last, we using the magnetic vector of the air-gap of sampling time and the sectional line in the machine for located rotor faults. Finally, we have dealt with how to identify an isolated and combined rotor faults in the indexed harmonics of magnetic flux density and compared by the healthy state of the machine.

Accurate extraction of single-diode photovoltaic (PV) model parameters is essential for reliable performance prediction and diagnostics, yet five-parameter identification from I-V data is ill-posed and computationally expensive. To develop and validate a hybrid analytical–metaheuristic approach that derives the diode ideality factor, saturation current, and photocurrent analytically while optimizing only series and shunt resistances, thereby reducing computational cost without sacrificing accuracy. I-V datasets were collected from a 9.54 kW grid-connected PV installation in Algiers, Algeria (15 operating points; 747–815 W m⁻²; 25.4–28.4 °C). Nine metaheuristics—Stellar Oscillation Optimizer, Enzyme Action Optimization, Grey Wolf Optimizer, Whale Optimization Algorithm, Cuckoo Search, Owl Search Algorithm, Improved War Strategy Optimization, Rüppell’s Fox Optimizer, and Artificial Bee Colony—were benchmarked against full five-parameter optimization and a Newton–Raphson baseline, using root-mean-squared error (RMSE) as the objective and wall-time as the efficiency metric. The hybrid scheme reduced the decision space from five to two parameters and lowered computational cost by ≈60–70% relative to full-parameter optimization while closely reproducing measured I-V/P-V curves. Across datasets, algorithms achieved RMSE ≈ 2.49 × 10⁻² − 2.78 × 10⁻². Rüppell’s Fox Optimizer offered the best overall trade-off (lowest average RMSE and fastest runtime), with Whale Optimization Algorithm a strong alternative (typical runtimes ≈ 107–112 s). Partitioning identification between closed-form physics and light-weight optimization yields robust, accurate, and efficient PV parameter estimation suitable for time-sensitive or embedded applications. Dynamic validation using 1498 real-world measurements across clear-sky and cloudy conditions demonstrates excellent performance: current prediction $R^2=0.9882$, power estimation $R^2=0.9730$, and voltage tracking $R^2=0.9613$. Comprehensive environmental analysis across a 39.2 °C temperature range and diverse irradiance conditions ($0\text{–}1014 \mathrm{W}/{\mathrm{m}}^2$) validates the method’s robustness for practical PV monitoring applications.