The use of artificial intelligence to automate PV module fault detection, diagnosis, and classification processes has gained interest for PV solar plants maintenance planning and reduction in expensive inspection and shutdown periods. The present article reports on the development of an adaptive neuro-fuzzy inference system (ANFIS) for PV fault classification based on statistical and mathematical features extracted from outdoor infrared thermography (IRT) and I-V measurements of thin-film PV modules. The selection of the membership function is shown to be essential to obtain a high classifier performance. Principal components analysis (PCA) is used to reduce the dimensions to speed up the classification process. For each type of fault, effective features that are highly correlated to the PV module’s operating power ratio are identified. Evaluation of the proposed methodology, based on datasets gathered from a typical PV plant, reveals that features extraction methods based on mathematical parameters and I-V measurements provide a 100% classification accuracy. On the other hand, features extraction based on statistical factors provides 83.33% accuracy. A novel technique is proposed for developing a correlation matrix between the PV operating power ratio and the effective features extracted online from infrared thermal images. This eliminates the need for offline I-V measurements to estimate the operating power ratio of PV modules.

Maintaining the safety and continuity of contemporary power systems depends critically on the accurate diagnosis of transformer failures. The most widely used diagnostic approach is still dissolved gas analysis (DGA); nevertheless, traditional ratio-based techniques, such as the Rogers’ ratio, rely on predefined thresholds and sometimes exhibit limited flexibility and unclear judgments under varied operating circumstances. This study suggests an optimization-oriented diagnostic approach that uses sophisticated metaheuristic algorithms to adaptively modify DGA gas ratio limitations in order to overcome these shortcomings. Four optimization schemes are formulated and comparatively assessed: the Artificial Protozoa Optimizer (APO), a hybrid Genetic Algorithm–Ant Colony Optimization model (GA–ACO), a hybrid Particle Swarm–Grey Wolf Optimization model (PSO–GWO), and a newly developed hybrid APO–PSO model. A dataset of 500 real-world DGA samples is used to evaluate the algorithms, and each optimization technique is conducted across 50 separate runs. The analysis focuses on statistical consistency, robustness, convergence characteristics, and diagnostic accuracy. With an average classification accuracy of around 96–97%, the suggested hybrid APO–PSO model outperforms standalone APO by about 2–3%, GA–ACO by 1–2%, and PSO–GWO by 1–2%, according to the numerical data. Furthermore, the APO–PSO scheme achieves more consistent behavior over repeated trials, reduced fitness variation, and quicker convergence. The statistical significance of these improvements is confirmed by statistical validation using the Friedman test and the Wilcoxon signed-rank test at a significance threshold of p < 0.05. Overall, the combination of APO’s strong global exploration with PSO’s efficient local exploitation produces a robust and adaptive diagnostic approach. The proposed framework enhances fault discrimination capability, reduces the likelihood of misclassification, and is suitable for both offline fault analysis and online transformer condition monitoring applications.

The facial expression generation continues to be interesting for researchers and scientists worldwide. Facial expressions are an excellent way of transmitting one's emotions or intentions to others, and they have been extensively studied in areas such as driver safety, human-computer interaction (HCI), deception detection, health care, and monitoring. The facial expression generation starts with a single neutral image and generates sequences of facial expression images, which are combined to create a video. Previous methods generated facial expression images of the same person. However, they still suffer from low accuracy and image quality. This article overcomes this problem using a novel hybrid model for facial expression video generation using fine-tuning and condition-generative model architectures to optimize the model's parameters. Results indicate that the proposed novel approach significantly improves the expression generation of the same person. The proposed method can reliably and accurately generate facial expressions, with a testing accuracy of 98.7% and a training accuracy of 99.9%.

Harmonic distortion in power systems, primarily caused by nonlinear loads, leads to significant power quality issues such as increased losses, reduced power factor, and equipment malfunctions. To mitigate these effects, passive power filters (PPFs) are widely employed due to their cost-effectiveness and simplicity. This paper presents an optimized design of a single-tuned passive filter (STPF) using the Firefly Algorithm (FFA) and its multi-objective extension, the Multi-Objective Firefly Algorithm (MOFA). The optimization aims to minimize both voltage total harmonic distortion (VTHD) and power loss and to maximize the power factor (PF) while complying with IEEE 519-2014 standards. The study evaluates the proposed method under two different industrial case studies with varying system parameters and harmonic profiles. Simulation results demonstrate that the proposed FFA-based optimization outperforms the Mixed Integer Distributed Ant Colony Optimization (MIDACO) method, achieving superior VTHD reduction, power loss minimization, and power factor enhancement. The MOFA approach provides a Pareto-optimal front, offering trade-offs among competing objectives. Comparative analysis confirms the efficiency, robustness, and faster convergence of FFA-based optimization, making it a promising approach for optimal filter design in power systems.

Traditional Dissolved Gas Analysis (DGA) interpretation methods such as Roger’s ratios and International Electrotechnical Commission (IEC) standards often classify many samples as “Not-Identified” due to their reliance on strict threshold values. These uncertainties limit their ability to accurately detect early-stage transformer faults. This study aims to develop a Fuzzy Logic (FL) diagnostic model that overcomes the “Not-Identified” problem in conventional methods, thereby improving fault classification and enabling earlier detection and maintenance actions in oil-immersed power transformers. A Mamdani-type FL system was applied to Roger’s and IEC interpretation methods to accommodate uncertainty in making crisp decisions. Sixty samples from different power transformers of the Egyptian Electricity Transmission Company (EETC) were examined for DGA analysis. The proposed fuzzy approach was used for the re-evaluation of cases that were previously determined as “Not-Identified” using the conventional method. The fuzzy model identified all of the samples (100%) by overcoming the “Not-Identified” cases and providing a definite fault type to all previously uncertain samples. In addition, it created a Fuzzy Severity Index (FSI) that can measure fault severity and helps maintenance schedule activities in order of risk. The developed fuzzy-mediated diagnostic system enhances the reliability and precision of transformer fault diagnosis, thus providing a practical and reliable tool for on-time maintenance planning and online condition monitoring.