RESUMEN
Accurate parameter identification of photovoltaic (PV) models is essential for the optimal operation and control of PV systems. However, PV cell modeling exhibits nonlinearity and involves numerous challenging-to-solve unknown parameters, thereby reducing the utilization efficiency of solar energy in PV systems. Therefore, this paper proposes an enhanced Snake algorithm (ISASO) that integrates Subtraction Average-Based Optimization (SABO) to address the shortcomings of traditional PV model parameter identification methods, such as low accuracy, slow convergence, and susceptibility to local optima. The SABO algorithm, which updates the positions of search agents using a consistent arithmetic mean position throughout the optimization process, demonstrates high convergence. By integrating SABO's global search strategy into the exploration phase of SO, the global search capability of SO is further enhanced, mitigating the risk of early local optima in the original SO. Additionally, the Tent chaotic map initialization method is incorporated into standard SO to improve the quality of the initial population and enhance population diversity. A dynamic learning factor and adaptive inertia weight strategy are also employed to accelerate the convergence speed of the SO algorithm, balancing its exploration and exploitation capabilities. To validate the performance of ISASO, it is applied to the CEC2005 benchmark functions and employed to identify the optimal parameters of various PV models. Statistical and analytical results reveal that ISASO markedly outperforms existing methods in parameter identification accuracy and reliability, achieving the lowest Root Mean Square Error (RMSE) values between standard and simulated data. Additionally, the superior performance of ISASO is further verified by comparative analysis with existing meta-heuristic algorithms and the Friedman mean ranking statistical method. Therefore, ISASO can be considered as a reliable and effective method to accurately estimate solar PV model parameters.
RESUMEN
Solar power is a renewable energy source, and its efficient development and utilization are important for achieving global carbon neutrality. However, partial shading conditions cause the output of PV systems to exhibit nonlinear and multipeak characteristics, resulting in a loss of output power. In this paper, we propose a novel Maximum Power Point Tracking (MPPT) technique for PV systems based on the Dung Beetle Optimization Algorithm (DBO) to maximize the output power of PV systems under various weather conditions. We performed a performance comparison analysis of the DBO technique with existing renowned MPPT techniques such as Squirrel Search Algorithm, Cuckoo search Optimization, Horse Herd Optimization Algorithm, Particle Swarm Optimization, Adaptive Factorized Particle Swarm Algorithm and Gray Wolf Optimization Hybrid Nelder-mead. The experimental validation is carried out on the HIL + RCP physical platform, which fully demonstrates the advantages of the DBO technique in terms of tracking speed and accuracy. The results show that the proposed DBO achieves 99.99% global maximum power point (GMPP) tracking efficiency, as well as a maximum improvement of 80% in convergence rate stabilization rate, and a maximum improvement of 8% in average power. A faster, more efficient and robust GMPP tracking performance is a significant contribution of the DBO controller.
RESUMEN
It is well known that welding dissimilar metals can play the advantages and characteristics of those different metals, but it is easy to encounter some problems. In this paper, the thermomechanical behavior of the weldolet-branch dissimilar steel joints in different welding cases is analyzed by establishing a three-dimensional finite element model, and the predicted thermal cycling and residual stresses are verified using experimental tools. The results show that the high temperature area and the heat affected zone on the side of the branch pipe are larger, and there is a large stress gradient at the fusion line on both sides of the weld. Too high or too low temperature between welding layers will cause large residual stress, thus, 200 °C is more suitable for the welding of weldolet-branch joints. The residual stresses of path-1, path-2 and path-3 have similar distributions at 0° and 180° sections, and the circumferential and axial residual stresses on the inner surface are larger than those on the outer surface. The residual stress on the inner and outer surfaces of path-3 is smaller than that of path-1 and path-2 at the 90° and 270° sections as a whole, and the residual stress at the 90° section reaches the minimum.
RESUMEN
In this paper, based on Simufact Welding finite element analysis software, a numerical simulation of the temperature and residual stress distribution of the weldolet-header multi-layer multi-pass welding process is carried out, and the simulation results are verified through experiments. The experimental results are in good agreement with the numerical simulation results, which proves the validity of the numerical simulation results. Through the results of the numerical simulation, the influence of the welding sequence and interlayer temperature on the temperature and residual stress distribution at different locations of the saddle-shaped weld was studied. The results show that the temperature and residual stress distribution on the header and weldolet are asymmetric, and the high-stress area of the saddle-shaped welded joint always appears at the saddle shoulder or saddle belly position. When the interlayer temperature is 300 °C, the peak residual stress reaches a minimum of 428.35 MPa. Adjusting the welding sequence can change the distribution trend of residual stress. There is no high-stress area on the first welding side of the two-stage welding path-2. The peak values of residual stresses for continuous welding path-1 and two-stage welding path-2 are 428.35 MPa and 434.01 MPa, respectively, which are very close to each other.