The optimal sizing of a hybrid energy system may be a difficult undertaking problem because of the huge number of structure settings and the irregular nature of solar radiation and wind power sources. This issue has a place with the classification of combinatorial enhancement, and its solution dependent on the classical technique may be a waste of time. In the present paper, the generation cost of isolated PV‐wind by bird system is minimized by increasing the capacity factor of the system by adjusting both of PV‐tilt angle and wind turbine hub height such that the average generated power is as near as possible to the generation peak. By considering the PV‐tilt angle and the turbine hub height, the diversity between the two generation patterns increases resulting in lower cost. The objective of the present paper is to find the optimal design of the PV‐wind hybrid system based on minimum cost criterion using a genetic algorithm. Also, it studies the effect of considering the PV‐tilt angle and wind turbine hub height as design parameters for different diversity levels of wind speed and sun irradiance patterns.

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 optimal sizing of a hybrid energy system may be a difficult undertaking problem, because of the huge number of structure settings and the irregular nature of solar radiation and wind power sources. This issue has a place with the classification of combinatorial enhancement, and its solution dependent on the classical technique may be a waste of time. This paper proposes a Genetic Algorithm (GA) methodology to find the optimal sizing of a hybrid Photovoltaic, Wind Turbine, Battery Storage, and Diesel Generators (PV/WT/BA/DG) energy system based on the number of PV modules, the number of wind turbines, the number of batteries, the number of diesel generators, the slope angle of PV panel and a hub height of wind turbine as the design parameters and study its effect on the Levelized Cost of Energy (LCOE). The proposed method aims to minimize the LCOE with high reliability of load supplying by increasing the design variables gradually. The proposed method will be tested at different sites with various metrological data to ensure its robustness. The results show that the LCOE decreases as the design parameters increase, also that the average wind-speed is inversely proportional to the LCOE of the site under study.